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European road users' risk perception and mobility : the SARTRE 4 survey

Profile image of Athanasios Akis TheofilatosAthanasios Akis TheofilatosProfile image of Anja PodlesekAnja Podlesek

2012

Abstract
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The SARTRE 4 survey aims to assess and compare road users' risk perception and mobility across multiple European countries to enhance road safety measures. The study highlights significant variations in road safety achievements and emphasizes the importance of understanding human factors influencing behaviors such as risk perception and motivation in road safety. It expands the focus from just car drivers to include powered two-wheelers, pedestrians, cyclists, and public transport users. The findings indicate notable trends in road safety outcomes, including an increase in powered two-wheeler usage and a concerning rise in motorcyclist fatalities in specific countries despite an overall decline in road deaths.

Figures (393)
Figure 1: Evolution of participating countries. First, the use of powered two wheelers increased in several countries (the number of vehicles in circulation — the “circulating park” - in Europe increased by 17% between 2003 and 2010'), most often in urban areas with dense traffic. Meanwhile, and fortunately, the number of people killed in car cras. hes decreased significantly over ten years in all European countries (41% mean decrease among SARTRE 4 countries); whereas the number of motorcyclists killed increased by 22%. Consequently, the proportion of motorcyclists among road deaths increased dramatically in some countries. This situation clearly showed that despite the overall good results, road users do not benefit equally from road safety improvements and the necessity to address more efficiently the question of motorcyclists’ safety on the road.
Figure 1: Evolution of participating countries. First, the use of powered two wheelers increased in several countries (the number of vehicles in circulation — the “circulating park” - in Europe increased by 17% between 2003 and 2010'), most often in urban areas with dense traffic. Meanwhile, and fortunately, the number of people killed in car cras. hes decreased significantly over ten years in all European countries (41% mean decrease among SARTRE 4 countries); whereas the number of motorcyclists killed increased by 22%. Consequently, the proportion of motorcyclists among road deaths increased dramatically in some countries. This situation clearly showed that despite the overall good results, road users do not benefit equally from road safety improvements and the necessity to address more efficiently the question of motorcyclists’ safety on the road.
Figure 2: Countries participating to SARTRE 4 survey.
Figure 2: Countries participating to SARTRE 4 survey.
Table 1: Data collection details by country.
Table 1: Data collection details by country.
Figure 1: Age structure of the driver population in the countries surveyed (Source: SARTRE 4). The age of a driver has an important effect on a number of road safety variables. It is often linked with the type of car, the number of kilometres travelled, driving style and involvement in car crashes (OECD, 2006; ETSC, 2011a). In SARTRE 4, the drivers were grouped into six age groups (See the age structure of the drivers in the countries, Figure 1). The age structure of the SARTRE 4 driver population was as follows:
Figure 1: Age structure of the driver population in the countries surveyed (Source: SARTRE 4). The age of a driver has an important effect on a number of road safety variables. It is often linked with the type of car, the number of kilometres travelled, driving style and involvement in car crashes (OECD, 2006; ETSC, 2011a). In SARTRE 4, the drivers were grouped into six age groups (See the age structure of the drivers in the countries, Figure 1). The age structure of the SARTRE 4 driver population was as follows:
What was surprising was the share of elderly drivers in the driver population. In recent years the population of those aged 65 and more was estimated at 17.4% of the EU-27 population and Eurostat (2011) forecasts suggest that society will continue to age’. As we analysed the demographic changes in the driver population, we expected older drivers to be a growing group because older people now keep their licenses longer, drive more miles and make up a bigger proportion of the population than in past years. However, when we look at the last two age groups, we saw that in 2010 older drivers made up a smaller proportion of all drivers. In SARTRE 4 survey, 9.5% of drivers were aged 65 and more. The differences between the countries are greater than in the case of young drivers. The share of drivers aged 65 and more grew the fastest in Italy (+6.2%), Germany (+5.8%), France (+5%) and the Netherlands (+3.5%) but fell in Ireland (-5.5%), Sweden (-3.8%) and Poland (-2.4%). More in-depth studies are needed to establish why a higher number of elderly people does not translate into a higher share of the driver population. It may be that these people stopped driving because they no longer feel confident behind the wheel or the costs of driving have gone up and the elderly can no longer afford it. Table 1: Percentage of drivers from different age groups in the overall driver population (Source: SARTRE 3 and 4).
What was surprising was the share of elderly drivers in the driver population. In recent years the population of those aged 65 and more was estimated at 17.4% of the EU-27 population and Eurostat (2011) forecasts suggest that society will continue to age’. As we analysed the demographic changes in the driver population, we expected older drivers to be a growing group because older people now keep their licenses longer, drive more miles and make up a bigger proportion of the population than in past years. However, when we look at the last two age groups, we saw that in 2010 older drivers made up a smaller proportion of all drivers. In SARTRE 4 survey, 9.5% of drivers were aged 65 and more. The differences between the countries are greater than in the case of young drivers. The share of drivers aged 65 and more grew the fastest in Italy (+6.2%), Germany (+5.8%), France (+5%) and the Netherlands (+3.5%) but fell in Ireland (-5.5%), Sweden (-3.8%) and Poland (-2.4%). More in-depth studies are needed to establish why a higher number of elderly people does not translate into a higher share of the driver population. It may be that these people stopped driving because they no longer feel confident behind the wheel or the costs of driving have gone up and the elderly can no longer afford it. Table 1: Percentage of drivers from different age groups in the overall driver population (Source: SARTRE 3 and 4).
The results confirm that the number of women drivers is growing and the change (by 3.5%) is statistically significant (y?= 47.806; p=0.00). The biggest increase in female drivers within the last eight years was recorded in: Estonia (27.4%), Greece (17.9%), Poland (11%) and Finland (10.6%) but there are also countries with a decrease in the population of women drivers over the last few years: Italy (-7.9%), Cyprus (-5.9%) and Belgium (-3.2%). Table 2: Percentage of women in the population of drivers in the countries surveyed (Source: SARTRE 3 and 4).
The results confirm that the number of women drivers is growing and the change (by 3.5%) is statistically significant (y?= 47.806; p=0.00). The biggest increase in female drivers within the last eight years was recorded in: Estonia (27.4%), Greece (17.9%), Poland (11%) and Finland (10.6%) but there are also countries with a decrease in the population of women drivers over the last few years: Italy (-7.9%), Cyprus (-5.9%) and Belgium (-3.2%). Table 2: Percentage of women in the population of drivers in the countries surveyed (Source: SARTRE 3 and 4).
Education has been key to the social and economic development of the European Union and its member states. There is a major need for well-qualified workers in all of Europe - and this demand will continue to rise. SARTRE 4 looked at how these general trends are reflected in the driver population, if at all (see Figure 2). Drivers were asked to choose one of four education options: No education, Primary education, Secondary education and Further education. Figure 2: Level of education in the driver population in the countries surveyed (Source: SARTRE 4).
Education has been key to the social and economic development of the European Union and its member states. There is a major need for well-qualified workers in all of Europe - and this demand will continue to rise. SARTRE 4 looked at how these general trends are reflected in the driver population, if at all (see Figure 2). Drivers were asked to choose one of four education options: No education, Primary education, Secondary education and Further education. Figure 2: Level of education in the driver population in the countries surveyed (Source: SARTRE 4).
Table 3: Level of education in the driver population in the countries surveyed (Source: SARTRE 3 and 4).
Table 3: Level of education in the driver population in the countries surveyed (Source: SARTRE 3 and 4).
Figure 3: Marital status in the driver population in the countries surveyed (Source: SARTRE 4). Concerning the marital status, the drivers were asked to choose one of five options (Single, Living is married, Married, Separated or Divorced, Widowed) which best fits their personal situation. Figure } shows the results for the countries participating in SARTRE 4.
Figure 3: Marital status in the driver population in the countries surveyed (Source: SARTRE 4). Concerning the marital status, the drivers were asked to choose one of five options (Single, Living is married, Married, Separated or Divorced, Widowed) which best fits their personal situation. Figure } shows the results for the countries participating in SARTRE 4.
The changes shown in the Table are statistically significant (y”,,.= 256.129; p= 0.00). In the 2010 river population there are more single drivers or drivers living with a partner. This change took place rimarily at the cost of married drivers. The highest percentage drop in the number of marriages was ecorded in Estonia (-19.3%), Slovenia (-15.7%), Hungary (-15.7%) and Sweden (-15.2%). While the irst two countries balanced the drop in marriages with a comparable increase in partner relations, n Hungary the difference was picked up by a higher number of people divorced or separated and in »weden by people who choose to be single. Only one country (Italy) saw an increase in married drivers n the period in question.
The changes shown in the Table are statistically significant (y”,,.= 256.129; p= 0.00). In the 2010 river population there are more single drivers or drivers living with a partner. This change took place rimarily at the cost of married drivers. The highest percentage drop in the number of marriages was ecorded in Estonia (-19.3%), Slovenia (-15.7%), Hungary (-15.7%) and Sweden (-15.2%). While the irst two countries balanced the drop in marriages with a comparable increase in partner relations, n Hungary the difference was picked up by a higher number of people divorced or separated and in »weden by people who choose to be single. Only one country (Italy) saw an increase in married drivers n the period in question.
Figure 4: Living area of drivers from the countries surveyed (Source: SARTRE 4). According to the UN State of the World Population 2007 report, for the first time in history in the middle of 2007 the majority of people worldwide were living in towns or cities. In the same report, Europe in 2007 is said to have 72% of the European population living in urban areas (EC, 2009). The data should be treated with some caution though. The urban-rural classification of population in internationally published statistics follows the national census definition which differs from one country or area to another. SARTRE 4 also used a descriptive form to identify the place of residence (rural/village, small town, suburban/city outskirts, urban/city/large town) and it may very well be that some of the categories were given different meanings in different countries (see Figure 4). In SARTRE 4, 37% of drivers live in large towns and 11.5% live in suburban areas (city outskirts), 25% live ina small town and 26.5% in rural areas (village). These results are quite consistent with those for the overall population, but obviously there are some major differences between the countries. For example in Israel 87% of all drivers live in major cities and 81% in Sweden compared to a mere 9% in Slovenia.
Figure 4: Living area of drivers from the countries surveyed (Source: SARTRE 4). According to the UN State of the World Population 2007 report, for the first time in history in the middle of 2007 the majority of people worldwide were living in towns or cities. In the same report, Europe in 2007 is said to have 72% of the European population living in urban areas (EC, 2009). The data should be treated with some caution though. The urban-rural classification of population in internationally published statistics follows the national census definition which differs from one country or area to another. SARTRE 4 also used a descriptive form to identify the place of residence (rural/village, small town, suburban/city outskirts, urban/city/large town) and it may very well be that some of the categories were given different meanings in different countries (see Figure 4). In SARTRE 4, 37% of drivers live in large towns and 11.5% live in suburban areas (city outskirts), 25% live ina small town and 26.5% in rural areas (village). These results are quite consistent with those for the overall population, but obviously there are some major differences between the countries. For example in Israel 87% of all drivers live in major cities and 81% in Sweden compared to a mere 9% in Slovenia.
Figure 5: Type of occupation of the drivers in the countries surveyed (Source: SARTRE 4). Most of the drivers are employed (52%) which together with self-employed drivers (14%) add up to 66% of drivers in employment. This figure is similar to the percentages in the overall populatiot (64.2%) (Eurostat, 2011b). There are, however, differences between the overall population and drive population in jobless figures. In the overall population in 2010, it was 9.7% compared to 5.5% amon; SARTRE 3 drivers. This can be easily explained with an economic rationale — it costs to have a ca and not everyone can afford it. What is interesting is the group of drivers who do not work whicl accounted for more than 28% of the driver population in 2010. This group includes pensioners (14 of overall driver population), students (9%) and people who for different reasons stay home (6%). Thi highest number of pensioners among those not working is in France (30% of overall driver population) Finland (26%) and Austria (24%). In some countries, however, students are the biggest group (Cypru:
Figure 5: Type of occupation of the drivers in the countries surveyed (Source: SARTRE 4). Most of the drivers are employed (52%) which together with self-employed drivers (14%) add up to 66% of drivers in employment. This figure is similar to the percentages in the overall populatiot (64.2%) (Eurostat, 2011b). There are, however, differences between the overall population and drive population in jobless figures. In the overall population in 2010, it was 9.7% compared to 5.5% amon; SARTRE 3 drivers. This can be easily explained with an economic rationale — it costs to have a ca and not everyone can afford it. What is interesting is the group of drivers who do not work whicl accounted for more than 28% of the driver population in 2010. This group includes pensioners (14 of overall driver population), students (9%) and people who for different reasons stay home (6%). Thi highest number of pensioners among those not working is in France (30% of overall driver population) Finland (26%) and Austria (24%). In some countries, however, students are the biggest group (Cypru:
It is expected that improved driver learner training methods and the demographic changes will continue to reduce the risks caused by inexperienced drivers in the years to come. However, it is also clear that the majority of drivers (more than 90% of the population) are not part of the education system any more and the success of the next road safety action programme of the European Union will depend on those road users. Table 7: Percentage breakdown of car experience (Source: SARTRE 3 and 4). In SARTRE 4, seven percent of the drivers have less than two years of car experience, 10% from } to 5 years, 13% from 6 tol0 years, 34 % from11 to 25 years and nearly 35% of the respondents have yeen driving for more than 25 years. The highest number of inexperienced drivers (less than 2 years) is n Cyprus (14%) and Sweden (13.8%), the lowest in Germany (2.8%) and the Netherlands (3.7%). The ypical inexperienced driver is usually a woman’, a person aged 17 to 24, employed, single, having at east secondary education and living in a city. Every twentieth driver from this group has been involved n at least one road accident and every fifth in one or more damage accidents over the last three years. owever, contrary to what could be expected, the numbers declared by this group are lower than those n the more experienced groups. When compared, the results of the last two SARTRE surveys show hat car experience distribution has not changed significantly (t= 0.831; p= 0.406 ; df= 36042) in the ast eight years (see Table 7). Car driving frequency
It is expected that improved driver learner training methods and the demographic changes will continue to reduce the risks caused by inexperienced drivers in the years to come. However, it is also clear that the majority of drivers (more than 90% of the population) are not part of the education system any more and the success of the next road safety action programme of the European Union will depend on those road users. Table 7: Percentage breakdown of car experience (Source: SARTRE 3 and 4). In SARTRE 4, seven percent of the drivers have less than two years of car experience, 10% from } to 5 years, 13% from 6 tol0 years, 34 % from11 to 25 years and nearly 35% of the respondents have yeen driving for more than 25 years. The highest number of inexperienced drivers (less than 2 years) is n Cyprus (14%) and Sweden (13.8%), the lowest in Germany (2.8%) and the Netherlands (3.7%). The ypical inexperienced driver is usually a woman’, a person aged 17 to 24, employed, single, having at east secondary education and living in a city. Every twentieth driver from this group has been involved n at least one road accident and every fifth in one or more damage accidents over the last three years. owever, contrary to what could be expected, the numbers declared by this group are lower than those n the more experienced groups. When compared, the results of the last two SARTRE surveys show hat car experience distribution has not changed significantly (t= 0.831; p= 0.406 ; df= 36042) in the ast eight years (see Table 7). Car driving frequency
B Nearly daily @ 1to4timesa week B 1to3timesa month @ Less than 1 time a month
B Nearly daily @ 1to4timesa week B 1to3timesa month @ Less than 1 time a month
The average SARTRE 4 driver reports an average of 15424 km driven in the previous year (M= 10000 km) which is comparable to SARTRE 3 (this is less by about 300 km, t= 1.600 ; p= 0.11; df= 36250). The average yearly performance differs substantially from country to country. The lowest average can be seen in Sweden (about 12068 km/person/year), Germany (13098 km) and Austria (13585 km), the highest - in Israel (21983 km/person/year), Serbia (18986 km) and Poland (18475 km). The Figure 8 shows driver responses broken into six categories.
The average SARTRE 4 driver reports an average of 15424 km driven in the previous year (M= 10000 km) which is comparable to SARTRE 3 (this is less by about 300 km, t= 1.600 ; p= 0.11; df= 36250). The average yearly performance differs substantially from country to country. The lowest average can be seen in Sweden (about 12068 km/person/year), Germany (13098 km) and Austria (13585 km), the highest - in Israel (21983 km/person/year), Serbia (18986 km) and Poland (18475 km). The Figure 8 shows driver responses broken into six categories.
Table 7: Number of kilometres driven annually in the driver population in the countries surveyed (Source: SARTRE 3 and 4).
Table 7: Number of kilometres driven annually in the driver population in the countries surveyed (Source: SARTRE 3 and 4).
The differences between the two surveys are statistically significant (Injury accidents: t= -2.039; = 0.041; df= 35449; Damage only accidents: t= 2.159; p= 0.031; df= 35448) although the changes are ninor. The results are somewhat surprising. As we know from European Commission’s data (CARE, 2011) 14 from 16 countries participating in SARTRE 3 and SARTRE 4 have improved their road safety yerformance. Despite the positive road safety developments, drivers said they had been involved in a similar number of injury accidents and damage only accidents just as 8 years ago. Poor memory is a y0ssible reason (the question covered the last three years) and/or different cognitive/social distortions cf. Wahlberg et al., 2010). Another possibility is some inaccuracy of accident data (e.g. underestimated slight injuries, different accident definitions in different countries). Table 8: Percentage of drivers involved in injury accidents and damage only accidents in the last 3 years (Source: SARTRE 3 and 4). SARTRE asked drivers whether in the last 3 years they had been involved in injury accidents anc lamage only accidents. Table 8 presents the results from the last two SARTRE surveys.
The differences between the two surveys are statistically significant (Injury accidents: t= -2.039; = 0.041; df= 35449; Damage only accidents: t= 2.159; p= 0.031; df= 35448) although the changes are ninor. The results are somewhat surprising. As we know from European Commission’s data (CARE, 2011) 14 from 16 countries participating in SARTRE 3 and SARTRE 4 have improved their road safety yerformance. Despite the positive road safety developments, drivers said they had been involved in a similar number of injury accidents and damage only accidents just as 8 years ago. Poor memory is a y0ssible reason (the question covered the last three years) and/or different cognitive/social distortions cf. Wahlberg et al., 2010). Another possibility is some inaccuracy of accident data (e.g. underestimated slight injuries, different accident definitions in different countries). Table 8: Percentage of drivers involved in injury accidents and damage only accidents in the last 3 years (Source: SARTRE 3 and 4). SARTRE asked drivers whether in the last 3 years they had been involved in injury accidents anc lamage only accidents. Table 8 presents the results from the last two SARTRE surveys.
Figure 9: Percentage changes in the number of injury accidents between 2001 and 2009 and the number of drivers from the different countries involved in injury accidents (Source: CARE, 2011, SARTRE 4).
Figure 9: Percentage changes in the number of injury accidents between 2001 and 2009 and the number of drivers from the different countries involved in injury accidents (Source: CARE, 2011, SARTRE 4).
Table 9: Cumulative results of the significance of varying results between two SARTRE surveys b variables and by countries. The objective of this analysis is to identify groups of highly inter-correlated variables that can be averaged into factors. These factors will have the property to preserve most of the data available from the sample while being much smaller in number. The actual decomposition of data into factors was made by principal component analysis (PCA). From the geometric perspective the solution of PCA is invariant upon rotation; so we have a choice to rotate the solution to our liking. We have chosen the varimax rotation which maximizes the variance of factor loadings for each item (varimax rotation) making the identification of factors easier. When selecting the factors the scree plot test was used, a method proposed by Cattell (1966). Five factors were identified during the analysis which explain 60.1% of the variance.
Table 9: Cumulative results of the significance of varying results between two SARTRE surveys b variables and by countries. The objective of this analysis is to identify groups of highly inter-correlated variables that can be averaged into factors. These factors will have the property to preserve most of the data available from the sample while being much smaller in number. The actual decomposition of data into factors was made by principal component analysis (PCA). From the geometric perspective the solution of PCA is invariant upon rotation; so we have a choice to rotate the solution to our liking. We have chosen the varimax rotation which maximizes the variance of factor loadings for each item (varimax rotation) making the identification of factors easier. When selecting the factors the scree plot test was used, a method proposed by Cattell (1966). Five factors were identified during the analysis which explain 60.1% of the variance.
Table 10: Matrix of components where variables with the highest factor loadings vs. other factors. We identified the following five factors (see Table 10) which capture most of the information contained in the 14 underlying variables they are defined on:
Table 10: Matrix of components where variables with the highest factor loadings vs. other factors. We identified the following five factors (see Table 10) which capture most of the information contained in the 14 underlying variables they are defined on:
To investigate whether concern about road safety was related to the objective level of road safety »f a country, a correlation to road fatalities per one million inhabitants (in 2009) was calculated. This correlation was not significant (r= .072, p= .770). For further investigation, the corresponding scatterplot was visually inspected (Figure 2). The medians of the variables were used to establish four juadrants distinguishing low vs. high concern and low vs. high road safety. Assuming that a particular amount of concern in the car driver population might be essential for the acceptance and effectiveness of road safety policies and measures, the Eastern European countries of Poland, the Czech Republic and Slovenia show the problematic combination of a relatively high fatality rate combined with a comparably low amount of concern within the population. In comparison, for example the South- Eastern countries Greece, Serbia and Cyprus show comparably high fatality rates but at the same time his is also reflected in a larger proportion of car drivers being ‘very’ concerned about the issue of road 19 fatryy
To investigate whether concern about road safety was related to the objective level of road safety »f a country, a correlation to road fatalities per one million inhabitants (in 2009) was calculated. This correlation was not significant (r= .072, p= .770). For further investigation, the corresponding scatterplot was visually inspected (Figure 2). The medians of the variables were used to establish four juadrants distinguishing low vs. high concern and low vs. high road safety. Assuming that a particular amount of concern in the car driver population might be essential for the acceptance and effectiveness of road safety policies and measures, the Eastern European countries of Poland, the Czech Republic and Slovenia show the problematic combination of a relatively high fatality rate combined with a comparably low amount of concern within the population. In comparison, for example the South- Eastern countries Greece, Serbia and Cyprus show comparably high fatality rates but at the same time his is also reflected in a larger proportion of car drivers being ‘very’ concerned about the issue of road 19 fatryy
Figure 2: Personal concern about road safety vs. fatality rate. The dashed lines indicate the medians of concern and fatality rate.
Figure 2: Personal concern about road safety vs. fatality rate. The dashed lines indicate the medians of concern and fatality rate.
Figure 3: Personal concern vs. perceived governmental concern about road safety. The diagonal lines indicate the difference in the percentages between individual concern and governmental concern.
Figure 3: Personal concern vs. perceived governmental concern about road safety. The diagonal lines indicate the difference in the percentages between individual concern and governmental concern.
Figure 4: Perceived road safety vs. fatality rate. In order to investigate whether the objective improvement in road safety was also subjectively perceived by the car drivers, the item “How much do you agree that our roads have become safer over the last 10 years?” was assessed. On average 55,2% ‘very’ (13,8%) or ‘fairly’ agreed. Still, large differences between countries emerged. While in Sweden, Austria and Spain about 80% of car drivers sensed an at least ‘fairly’ improvement, less than one third of car drivers from Hungary, the Czech Republic and Greece did so.
Figure 4: Perceived road safety vs. fatality rate. In order to investigate whether the objective improvement in road safety was also subjectively perceived by the car drivers, the item “How much do you agree that our roads have become safer over the last 10 years?” was assessed. On average 55,2% ‘very’ (13,8%) or ‘fairly’ agreed. Still, large differences between countries emerged. While in Sweden, Austria and Spain about 80% of car drivers sensed an at least ‘fairly’ improvement, less than one third of car drivers from Hungary, the Czech Republic and Greece did so.
Table 3: Percentage indicating certain factors to be at least ‘often’ the cause of car drivers being involved in road accidents. For each country the rank of each factor is indicated in the column “rk”,
Table 3: Percentage indicating certain factors to be at least ‘often’ the cause of car drivers being involved in road accidents. For each country the rank of each factor is indicated in the column “rk”,
Personal Driving Style Table 4: Changes from SARTRE 3 to SARTRE 4 in the proportion of respondents indicating certain factors to be at least ‘often’ the cause of car drivers being involved in road accidents. Significant changes (2 test) are indicated in bold type.
Personal Driving Style Table 4: Changes from SARTRE 3 to SARTRE 4 in the proportion of respondents indicating certain factors to be at least ‘often’ the cause of car drivers being involved in road accidents. Significant changes (2 test) are indicated in bold type.
Figure 8: Frequency distribution and changes since SARTRE 3 for the item “When driving a car, how often do you give way to a pedestrian at pedestrian crossings?”
Figure 8: Frequency distribution and changes since SARTRE 3 for the item “When driving a car, how often do you give way to a pedestrian at pedestrian crossings?”
Table 5: Results of binary logistic regression analysis with indicated behaviors as independent and accidents as dependent variable. All df= 1 and p< ,001.
Table 5: Results of binary logistic regression analysis with indicated behaviors as independent and accidents as dependent variable. All df= 1 and p< ,001.
Wdrivethrough amber G followtoo closely Bgive way to pedestrian Duse handheld phone The relation between risky driving behavior and accident involvement based on the proportions of drivers indicating a risky driving behavior is also displayed in Figure 9. This figure in addition includes the differences for age and gender. It can be seen that for all four behaviors there are effects of age (Kruskal-Wallis-Test: all y7> 95; p< ,001) and gender (all y°> 12; p< ,001). Younger drivers as well as male drivers seem to be more prone of performing risky driving behavior than older drivers and female drivers. This is well in line with established findings from the literature concerning risky driving behavior (Rhodes & Pivik, 2011).
Wdrivethrough amber G followtoo closely Bgive way to pedestrian Duse handheld phone The relation between risky driving behavior and accident involvement based on the proportions of drivers indicating a risky driving behavior is also displayed in Figure 9. This figure in addition includes the differences for age and gender. It can be seen that for all four behaviors there are effects of age (Kruskal-Wallis-Test: all y7> 95; p< ,001) and gender (all y°> 12; p< ,001). Younger drivers as well as male drivers seem to be more prone of performing risky driving behavior than older drivers and female drivers. This is well in line with established findings from the literature concerning risky driving behavior (Rhodes & Pivik, 2011).
Figure 10: Integrated measure of a risky driving style (drive through amber, follow too closely, stop at pedestrian crossing). Larger numbers indicate a more risky driving style. Plus and minus signs indicate significant changes in relation to SARTRE 3 (t-Test, p< ,05).
Figure 10: Integrated measure of a risky driving style (drive through amber, follow too closely, stop at pedestrian crossing). Larger numbers indicate a more risky driving style. Plus and minus signs indicate significant changes in relation to SARTRE 3 (t-Test, p< ,05).
Figure 11: Average change in driving style versus reduction of fatality rate. Attitudes concerning Penalties for Traffic Offences For five typical traffic offences (speeding, drink-driving, not using restraint systems, not wearing a helmet on a motorcycle, using a handheld phone while driving) it was assessed how much the respondents agreed to the implementation of much more severe penalties. More than three quarters of the car drivers ‘strongly agreed’ or ‘agreed’ concerning drink-driving (84%) and helmet use (78%). For alcohol even in the countries with the lowest agreement, more than 70% agree on an increase of penalty severity for drink-driving. For handheld phone use (63%) and the use of restraint systems (62%) the agreement was somewhat lower, while only about half of the car drivers agreed to the implementation concerning speeding offences (52%).
Figure 11: Average change in driving style versus reduction of fatality rate. Attitudes concerning Penalties for Traffic Offences For five typical traffic offences (speeding, drink-driving, not using restraint systems, not wearing a helmet on a motorcycle, using a handheld phone while driving) it was assessed how much the respondents agreed to the implementation of much more severe penalties. More than three quarters of the car drivers ‘strongly agreed’ or ‘agreed’ concerning drink-driving (84%) and helmet use (78%). For alcohol even in the countries with the lowest agreement, more than 70% agree on an increase of penalty severity for drink-driving. For handheld phone use (63%) and the use of restraint systems (62%) the agreement was somewhat lower, while only about half of the car drivers agreed to the implementation concerning speeding offences (52%).
Conclusion Table 6: Percentages of respondents that either ‘strongly agree’ or ‘agree’ to the statement “Penalties for speeding/drink-driving offences should be much more severe”. Significant changes from SARTRE 3 tn CADTDE A arn indinatad in hald hinn
Conclusion Table 6: Percentages of respondents that either ‘strongly agree’ or ‘agree’ to the statement “Penalties for speeding/drink-driving offences should be much more severe”. Significant changes from SARTRE 3 tn CADTDE A arn indinatad in hald hinn
Figure 4: Percentage that does not believe that driving 20 km/h over the speed limit in a residential area would lead to them being stopped and fined by the police.
Figure 4: Percentage that does not believe that driving 20 km/h over the speed limit in a residential area would lead to them being stopped and fined by the police.
Figure 5: Percentage that agree “very” or “fairly” much with that most of their friends would drive 20 km/h over the speed limit in a residential area.
Figure 5: Percentage that agree “very” or “fairly” much with that most of their friends would drive 20 km/h over the speed limit in a residential area.
Figure 6: Percentage that answered “very often” or “always” when asked how likely it would be to drive at 20 km/h over the speed limit in a residential area the next month.
Figure 6: Percentage that answered “very often” or “always” when asked how likely it would be to drive at 20 km/h over the speed limit in a residential area the next month.
Figure 9: Percentage that think other car drivers «very often» or «always» speed on country roads.
Figure 9: Percentage that think other car drivers «very often» or «always» speed on country roads.
Figure 10: Percentage that think other car drivers «very often» or «always» speed in built-up areas.
Figure 10: Percentage that think other car drivers «very often» or «always» speed in built-up areas.
The survey also included questions about speed enforcement and if they had received any penalties for speeding. Figure 11 shows the results from the question regarding how likely it is that they would be checked for speeding. Figure 11: Percentage that answered «never» or «rarely» when asked how likely it would be to be checked for speeding on a typical journey.
The survey also included questions about speed enforcement and if they had received any penalties for speeding. Figure 11 shows the results from the question regarding how likely it is that they would be checked for speeding. Figure 11: Percentage that answered «never» or «rarely» when asked how likely it would be to be checked for speeding on a typical journey.
Figure 14: Drivers who had not been fined for breaking the speed limit during the last 3 years. Comparison of data from 2002 and 2010. The drivers had to indicate if they had been fined or received other penalties during the last 3 years for speeding, drivers in the Netherlands are most likely to have received a fined and drivers in Greece least likely (see Figure 13). Men have also been penalised more than women, 28% compared to 17% (y= 231.00; p<0.001), and those aged 25-34 years more than the oldest age group (27% and 17% respectively) (y= 73.09; p<0.001). Since drivers’ perception of being checked for speeding might be influenced by whether they had received some form of punishment in the past, the correlation coefficient was calculated. Drivers who had been fined are more likely to believe that they would be checked, but the correlation is low (r= .17; p <0.01).
Figure 14: Drivers who had not been fined for breaking the speed limit during the last 3 years. Comparison of data from 2002 and 2010. The drivers had to indicate if they had been fined or received other penalties during the last 3 years for speeding, drivers in the Netherlands are most likely to have received a fined and drivers in Greece least likely (see Figure 13). Men have also been penalised more than women, 28% compared to 17% (y= 231.00; p<0.001), and those aged 25-34 years more than the oldest age group (27% and 17% respectively) (y= 73.09; p<0.001). Since drivers’ perception of being checked for speeding might be influenced by whether they had received some form of punishment in the past, the correlation coefficient was calculated. Drivers who had been fined are more likely to believe that they would be checked, but the correlation is low (r= .17; p <0.01).
Figure 15: Percentage that were «not much» or «not at all» in favour of more “30 km/h” zones in built-up areas.
Figure 15: Percentage that were «not much» or «not at all» in favour of more “30 km/h” zones in built-up areas.
Figure 16: Percentage that disagreed/strongly disagreed with that penalty for speeding offences should be more severe.
Figure 16: Percentage that disagreed/strongly disagreed with that penalty for speeding offences should be more severe.
Figure 17: Tickets for speeding and percentage of subjects that the last three years had been fined or else punished for speeding. perceived likelihood of being charged. The results reveal only one significant relationship. Countries with at greater number of speed tickets per number of cars also have the largest percentage of drivers who report being fined in the last three years (Figure 17).
Figure 17: Tickets for speeding and percentage of subjects that the last three years had been fined or else punished for speeding. perceived likelihood of being charged. The results reveal only one significant relationship. Countries with at greater number of speed tickets per number of cars also have the largest percentage of drivers who report being fined in the last three years (Figure 17).
Table 1: Logistic regression for intention to speed (in brackets reference category).
Table 1: Logistic regression for intention to speed (in brackets reference category).
z= z-score for test of effect in factor change; P>|z|= p-value for z-test; Odds ratio= factor change in odds for unit increase in predictor variable, holding all other variables constant.
z= z-score for test of effect in factor change; P>|z|= p-value for z-test; Odds ratio= factor change in odds for unit increase in predictor variable, holding all other variables constant.
+Controlling for all the other mentioned factors in Table 2. The odds of breaking the speed limit (self-reported) can be divided into four main groups; the first shows countries where the chance increase by more than 200%, the second when the same is between 200 and 100%. In the third group the odds decreases, 1.e. less than 100%, and the last group present countries with no significant effect:
+Controlling for all the other mentioned factors in Table 2. The odds of breaking the speed limit (self-reported) can be divided into four main groups; the first shows countries where the chance increase by more than 200%, the second when the same is between 200 and 100%. In the third group the odds decreases, 1.e. less than 100%, and the last group present countries with no significant effect:
Figure 1: Frequency of Driving a Car after Having Drunk, Even a Small Amount of Alcohol in Past Month in %.
Figure 1: Frequency of Driving a Car after Having Drunk, Even a Small Amount of Alcohol in Past Month in %.
Figure 2: Frequency of Driving over the Legal Alcohol Limit in Past Month in %.
Figure 2: Frequency of Driving over the Legal Alcohol Limit in Past Month in %.
Figure 3: Percentage of car drivers who believe you can drink and drive if you drive carefully. The country comparison shows that the differences between countries are large. The lowest number of drivers who believe they can drink and drive once they are careful is found in participating countries such as Greece (2.2%), Hungary (2.6%), Sweden (3.1%), Finland (3.7%) and Ireland (4%). A group of 8 countries composed of the Netherlands (10.6%), Austria (10.7%), France (11.9%), Serbia (12.5%), Israel (13.1%), Italy (17.2%), Cyprus (17.5%), and Belgium (17.5%) are found to have highest number of car drivers who believe they can drink and drive once they are careful (Figure 3).
Figure 3: Percentage of car drivers who believe you can drink and drive if you drive carefully. The country comparison shows that the differences between countries are large. The lowest number of drivers who believe they can drink and drive once they are careful is found in participating countries such as Greece (2.2%), Hungary (2.6%), Sweden (3.1%), Finland (3.7%) and Ireland (4%). A group of 8 countries composed of the Netherlands (10.6%), Austria (10.7%), France (11.9%), Serbia (12.5%), Israel (13.1%), Italy (17.2%), Cyprus (17.5%), and Belgium (17.5%) are found to have highest number of car drivers who believe they can drink and drive once they are careful (Figure 3).
Figure 4: Number of Alcohol Units Which is Within Legal Limit for Driving.
Figure 4: Number of Alcohol Units Which is Within Legal Limit for Driving.
Figure 6: Percentage point change between SARTRE 3 and SARTRE 4 in support for a ban of alcohol.
Figure 6: Percentage point change between SARTRE 3 and SARTRE 4 in support for a ban of alcohol.
Figure 7: In the Past 3 Years, How Many Times Were You Checked for Drink Driving?
Figure 7: In the Past 3 Years, How Many Times Were You Checked for Drink Driving?
Figure 8: Predicted probability of drink driving over the legal limit (controlling for all the other mentioned factors).
Figure 8: Predicted probability of drink driving over the legal limit (controlling for all the other mentioned factors).
Analysis by Country Grouping
Analysis by Country Grouping
*** p<0.001, +Controlling for all the other mentioned factors in Table 1.
*** p<0.001, +Controlling for all the other mentioned factors in Table 1.
More than 4 in 5 car drivers understand the dangers of taking medication that carries a “warning: it may influence your driving ability” when driving. However, 10% sometimes or often take such medication when driving. The highest proportion of respondents who sometime or often take such medication are found in France (26%), the Netherlands (18%), Italy (17%), Spain (14%) and Belgium (13%) and the lowest frequency are found in Greece (3%) and Slovenia (5%). There is no clear age effect (see Figure 9 & Figure 10). Figure 9: How Dangerous is it to Drive While Taking Mediation that Carries the Warning —May Influence your Driving.
More than 4 in 5 car drivers understand the dangers of taking medication that carries a “warning: it may influence your driving ability” when driving. However, 10% sometimes or often take such medication when driving. The highest proportion of respondents who sometime or often take such medication are found in France (26%), the Netherlands (18%), Italy (17%), Spain (14%) and Belgium (13%) and the lowest frequency are found in Greece (3%) and Slovenia (5%). There is no clear age effect (see Figure 9 & Figure 10). Figure 9: How Dangerous is it to Drive While Taking Mediation that Carries the Warning —May Influence your Driving.
Figure 10: Frequency of Driving While Taking Such Medication in %.
Figure 10: Frequency of Driving While Taking Such Medication in %.
Driving whilst fatigued is prevalent among all the participating countries and is reported more by males and decreases with age. The country comparison shows that there differences between countries among age groups. In Cyprus prevalence of driver fatigue was almost 40% of the car driver population while in Slovenia, the Netherlands and Ireland and Germany less than 20% reported this behaviour. When investigating differences between countries, there is only a medium variability without any obvious influence of regional location within the EU (north vs. south; east vs. west), see Figure 11. Due to large differences in daylight between Northern and Southern Europe, which might be related to the evolution of fatigue, one might have expected a systematic influence * Drivers report driven when at least ‘sometimes’ felt too tired to drive.
Driving whilst fatigued is prevalent among all the participating countries and is reported more by males and decreases with age. The country comparison shows that there differences between countries among age groups. In Cyprus prevalence of driver fatigue was almost 40% of the car driver population while in Slovenia, the Netherlands and Ireland and Germany less than 20% reported this behaviour. When investigating differences between countries, there is only a medium variability without any obvious influence of regional location within the EU (north vs. south; east vs. west), see Figure 11. Due to large differences in daylight between Northern and Southern Europe, which might be related to the evolution of fatigue, one might have expected a systematic influence * Drivers report driven when at least ‘sometimes’ felt too tired to drive.
When investigating the number of car drivers within the four clusters for each country (Figure 13) a considerable variance between countries evolves. When summing up the frequent copes and the dysfunctional copes into one group that applies rather unsafe countermeasures when fatigued, in Cyprus, Austria and Estonia more than 70% of the car driver population belong to this group, while this is only the case for less than 40% in Belgium, Slovenia, the Netherlands and France. Interestingly, the Netherlands is the only country that reported a dedicated fatigue driving campaign within the timeframe since the last SARTRE edition.
When investigating the number of car drivers within the four clusters for each country (Figure 13) a considerable variance between countries evolves. When summing up the frequent copes and the dysfunctional copes into one group that applies rather unsafe countermeasures when fatigued, in Cyprus, Austria and Estonia more than 70% of the car driver population belong to this group, while this is only the case for less than 40% in Belgium, Slovenia, the Netherlands and France. Interestingly, the Netherlands is the only country that reported a dedicated fatigue driving campaign within the timeframe since the last SARTRE edition.
Table 3: Characteristics of the identified clusters.
Table 3: Characteristics of the identified clusters.
Figure 13: Proportion of car drivers belonging to each of the four clusters within each country. Factors affecting driver fatigue
Figure 13: Proportion of car drivers belonging to each of the four clusters within each country. Factors affecting driver fatigue
Complementary table : Factors affecting drink-driving behaviours in Southern countries. Complementary table z= z-score for test of effect in factor change; P>\z|= p-value for z-test; Odds ratio= factor change in odds for unit increase in predictor variable, holding all other variables constant.
Complementary table : Factors affecting drink-driving behaviours in Southern countries. Complementary table z= z-score for test of effect in factor change; P>\z|= p-value for z-test; Odds ratio= factor change in odds for unit increase in predictor variable, holding all other variables constant.
Figure 1: Examples for Driver Assistance System classification (after Schulze et al., 2005). While some systems are only offering information to a driver, others could even interfere with his control over vehicle. Stevens (2009) introduces a fourfold classification of in-vehicle functions covering IVIS and ADAS:
Figure 1: Examples for Driver Assistance System classification (after Schulze et al., 2005). While some systems are only offering information to a driver, others could even interfere with his control over vehicle. Stevens (2009) introduces a fourfold classification of in-vehicle functions covering IVIS and ADAS:
Navigation systems, portable or fixed are not yet self-understandable part of the cars, with exception of the more expensive ones, though portable devices are not so expensive. Also the frequency of driving and knowledge of destinations may influence their use. This is revealed also in the answers of our participants.
Navigation systems, portable or fixed are not yet self-understandable part of the cars, with exception of the more expensive ones, though portable devices are not so expensive. Also the frequency of driving and knowledge of destinations may influence their use. This is revealed also in the answers of our participants.
Figure 3: Presence of the ABS in own car in different countries (y?= 653,083 ; p= 0.000). Anti-Lock Braking System (ABS) is almost necessary function of modern cars. It exists, but is invisible to the driver, perhaps only sensed when in action. Especially in connection with ABS risk compensating mechanisms should be considered.
Figure 3: Presence of the ABS in own car in different countries (y?= 653,083 ; p= 0.000). Anti-Lock Braking System (ABS) is almost necessary function of modern cars. It exists, but is invisible to the driver, perhaps only sensed when in action. Especially in connection with ABS risk compensating mechanisms should be considered.
Figure 4: Presence of the seat belt reminder in own car in different countries (y= 820,845 ; p= 0.000).
Figure 4: Presence of the seat belt reminder in own car in different countries (y= 820,845 ; p= 0.000).
One of the first questions that appeared in connection with ITS devices was their interaction with drivers’ behavior, namely would — as proposed by Wilde’s risk homeostasis theory - drivers with ABS in their cars compensate with faster driving. Speed ticketing is an indicator of speeding and greater relative numbers of punished drivers in cars with ABS in comparison to those without it can give us at least partial answer, see Figure 5. Figure 5: Crosstabulation of ABS presence in own car and speed ticketing (y2= 68,257; p= 0.000). Variable was dichotomised, namely categories ‘Yes, only fine’ and ‘Yes, other with/out fine’ were joined, due to the relatively small numbers of speed ticketed drivers.
One of the first questions that appeared in connection with ITS devices was their interaction with drivers’ behavior, namely would — as proposed by Wilde’s risk homeostasis theory - drivers with ABS in their cars compensate with faster driving. Speed ticketing is an indicator of speeding and greater relative numbers of punished drivers in cars with ABS in comparison to those without it can give us at least partial answer, see Figure 5. Figure 5: Crosstabulation of ABS presence in own car and speed ticketing (y2= 68,257; p= 0.000). Variable was dichotomised, namely categories ‘Yes, only fine’ and ‘Yes, other with/out fine’ were joined, due to the relatively small numbers of speed ticketed drivers.
Figure 6: Proportion of reported speed tickets among ABS and non ABS users in different countries. With * are signed countries where drivers with ABS have relatively more speed ticketing than drivers without ABS. Statistically significant are 7’s for Czech Republic, France, Greece, Hungary, Italy, Netherlands, Poland and Slovenia.
Figure 6: Proportion of reported speed tickets among ABS and non ABS users in different countries. With * are signed countries where drivers with ABS have relatively more speed ticketing than drivers without ABS. Statistically significant are 7’s for Czech Republic, France, Greece, Hungary, Italy, Netherlands, Poland and Slovenia.
Figure 7: Crosstabulation of ABS presence in different age groups (77= 27,846; p= 0.000). Regarding age, only differences for ABS presence will be presented.
Figure 7: Crosstabulation of ABS presence in different age groups (77= 27,846; p= 0.000). Regarding age, only differences for ABS presence will be presented.
For all ITS devices it could be said that they are the least present in oldest and youngest groups (see Figure 7). While the first have probably aged cars, the second could not afford the better ones. Figure 8: Crosstabulation of navigation system presence in different professional groups (7?= 265,071; p= 0.000).
For all ITS devices it could be said that they are the least present in oldest and youngest groups (see Figure 7). While the first have probably aged cars, the second could not afford the better ones. Figure 8: Crosstabulation of navigation system presence in different professional groups (7?= 265,071; p= 0.000).
Figure 9: Crosstabulation of ABS system presence in different professional groups (y?= 170,259; p= 0.000).
Figure 9: Crosstabulation of ABS system presence in different professional groups (y?= 170,259; p= 0.000).
Figure 10: Favorability of using speed limiting devices in different countries. The favourability of speed limiting device (very + fairly) ranges from at least 41,7% (Sweden) to 83,7% (Ireland), see Figure 10. The majority of participants are therefore favouring this device. On the one extreme we have Sweden (41,7%), Netherlands (42,9%), Poland (46,9%) and Finland (47,4%) and on the other Ireland (83,7%), Greece (81,3%), Estonia (76,9%), Cyprus (73,2%), Italy (73,3%), Serbia (72,4%), Spain (70,9%), and Israel (65,7%), so a division between northern and southern countries, the later being more in favour of speed limiting device.
Figure 10: Favorability of using speed limiting devices in different countries. The favourability of speed limiting device (very + fairly) ranges from at least 41,7% (Sweden) to 83,7% (Ireland), see Figure 10. The majority of participants are therefore favouring this device. On the one extreme we have Sweden (41,7%), Netherlands (42,9%), Poland (46,9%) and Finland (47,4%) and on the other Ireland (83,7%), Greece (81,3%), Estonia (76,9%), Cyprus (73,2%), Italy (73,3%), Serbia (72,4%), Spain (70,9%), and Israel (65,7%), so a division between northern and southern countries, the later being more in favour of speed limiting device.
Black Box Figure 11: Favorability of using black box in different countries.
Black Box Figure 11: Favorability of using black box in different countries.
Figure 12: Favorability of Alco-Lock in different countries.
Figure 12: Favorability of Alco-Lock in different countries.
Figure 13: Favourability of Alco-Lock for recidivous drivers in different countries Alco-lock for Recidivists Driver
Figure 13: Favourability of Alco-Lock for recidivous drivers in different countries Alco-lock for Recidivists Driver
Figure 14: Favourability of fatigue detection devices in different countries. Fatigue Detection Devices The favourability of fatigue detection devices (very + fairly) ranges from at least 60,1% (Austria) to 89,8% (Ireland), see Figure 14. The majority of participants are therefore favouring this device, the differences between countries being relatively small. On the one extreme we have Austria (60,1%), and Finland (65,7%), and on the other Ireland (89,8%), Spain (86,7%), Slovenia (83,1%), Italy (82,9%), Sweden (79,2%), and Israel (73,3%), so again a different division than previously, the later countries being more in favour of fatigue detection devices. Average favorability for fatigue detection devices was 76.8%.
Figure 14: Favourability of fatigue detection devices in different countries. Fatigue Detection Devices The favourability of fatigue detection devices (very + fairly) ranges from at least 60,1% (Austria) to 89,8% (Ireland), see Figure 14. The majority of participants are therefore favouring this device, the differences between countries being relatively small. On the one extreme we have Austria (60,1%), and Finland (65,7%), and on the other Ireland (89,8%), Spain (86,7%), Slovenia (83,1%), Italy (82,9%), Sweden (79,2%), and Israel (73,3%), so again a different division than previously, the later countries being more in favour of fatigue detection devices. Average favorability for fatigue detection devices was 76.8%.
Table 2: ANOVA for favourability of ITS devices among males and females with descriptives (Mean and SD).
Table 2: ANOVA for favourability of ITS devices among males and females with descriptives (Mean and SD).
It seems that older participant are more in favour of ITS devices than younger, perhaps becaus they perceive them more as a support than as an interference with their driving activity. Table 3: ANOVA for favourability of ITS devices among different age groups with descriptives (Mean).
It seems that older participant are more in favour of ITS devices than younger, perhaps becaus they perceive them more as a support than as an interference with their driving activity. Table 3: ANOVA for favourability of ITS devices among different age groups with descriptives (Mean).
While drivers of weaker cars are more in favour of speed limiting devices, drivers of stronger cars are less prone toward them, probably they prefer to show the full strength of their machines (see Table 5). Regarding other devices the differences between owners of cars with different machine size are much smaller.
While drivers of weaker cars are more in favour of speed limiting devices, drivers of stronger cars are less prone toward them, probably they prefer to show the full strength of their machines (see Table 5). Regarding other devices the differences between owners of cars with different machine size are much smaller.
Figure 15: K-means analysis of favourability (1= very - 4= not at all) of ITS devices (N = 5083; N = 2062; N = 5205). Cluster 1 Cluster2 Cluster 3 Living area and education do not influence favorability to ITS devices very much; the people without formal education express the highest preference for them.
Figure 15: K-means analysis of favourability (1= very - 4= not at all) of ITS devices (N = 5083; N = 2062; N = 5205). Cluster 1 Cluster2 Cluster 3 Living area and education do not influence favorability to ITS devices very much; the people without formal education express the highest preference for them.
While in previous analyses by countries attitudes toward particular ITS devices were presented, here is a more general view (see Table 6). Participants from Cluster 3 (the most favorable to ITS devices) were prevailing in general (42,1%), and especially in Ireland (69,7%), Italy (52,9%), Estonia (49,8%), Cyprus (49,1%), Slovenia (46,6%), Spain (46,4%), Serbia (45,8%), Greece (45,6%), and France (45,6%). The least favorable (Cluster 2) was the attitude of participants from Austria (29,5%), Belgium (24,3%), Poland (21,5%) and Czech Republic (20,60%). It appeared that favorability of ITS devices is increasing from Northern toward Southern countries, Estonia and Ireland being an exception.
While in previous analyses by countries attitudes toward particular ITS devices were presented, here is a more general view (see Table 6). Participants from Cluster 3 (the most favorable to ITS devices) were prevailing in general (42,1%), and especially in Ireland (69,7%), Italy (52,9%), Estonia (49,8%), Cyprus (49,1%), Slovenia (46,6%), Spain (46,4%), Serbia (45,8%), Greece (45,6%), and France (45,6%). The least favorable (Cluster 2) was the attitude of participants from Austria (29,5%), Belgium (24,3%), Poland (21,5%) and Czech Republic (20,60%). It appeared that favorability of ITS devices is increasing from Northern toward Southern countries, Estonia and Ireland being an exception.
On its own speeding control measures, concern for road accidents and especially attitude to speeding penalty explain the greatest part of variability (about 24%) in attitudes toward speed limiting device. It seems that motivation for this device is still more extrinsic than intrinsic, what means that there is a need for appropriate actions, education, campaign, etc.
On its own speeding control measures, concern for road accidents and especially attitude to speeding penalty explain the greatest part of variability (about 24%) in attitudes toward speed limiting device. It seems that motivation for this device is still more extrinsic than intrinsic, what means that there is a need for appropriate actions, education, campaign, etc.
Table 8: Summary of Forward Stepwise Regression for Black Box.
Table 8: Summary of Forward Stepwise Regression for Black Box.
b. Predictors: (Constant), Drink-driving penalty (B= 0.33), Road accidents (B= 0.143). Table 9: Summary of Forward Stepwise Regression for Alco-Lock.
b. Predictors: (Constant), Drink-driving penalty (B= 0.33), Road accidents (B= 0.143). Table 9: Summary of Forward Stepwise Regression for Alco-Lock.
Table 10: Summary of Forward Stepwise Regression for Alco-Lock for recidivists. Independent variables included into the model explain only around 10% of variability in attitudes toward a Alco-Lock for recidivists, the most important being again attitudes toward drink-driving penalty and disagreement with the opinion that you could drink if drive carefully (see Table 10).
Table 10: Summary of Forward Stepwise Regression for Alco-Lock for recidivists. Independent variables included into the model explain only around 10% of variability in attitudes toward a Alco-Lock for recidivists, the most important being again attitudes toward drink-driving penalty and disagreement with the opinion that you could drink if drive carefully (see Table 10).
Figure 17: Not in favour of using a black box (%), comparison data from 2002 and 2010 (1= “very”, 2= ”Fairly”, 3= "Not much” and 4= Not at all”).
Figure 17: Not in favour of using a black box (%), comparison data from 2002 and 2010 (1= “very”, 2= ”Fairly”, 3= "Not much” and 4= Not at all”).
Table 1: Gender and age of motorcyclists separated for countries (in %). As Table 1 shows there are large differences in the proportion of male or female riders. The largest proportions of male rides are in Hungary and Serbia. The largest proportions of female riders are in Italy, France and Netherlands. The largest proportions of younger riders are in Serbia and Israel, and the largest proportions of older riders are found in Italy and Germany.
Table 1: Gender and age of motorcyclists separated for countries (in %). As Table 1 shows there are large differences in the proportion of male or female riders. The largest proportions of male rides are in Hungary and Serbia. The largest proportions of female riders are in Italy, France and Netherlands. The largest proportions of younger riders are in Serbia and Israel, and the largest proportions of older riders are found in Italy and Germany.
Education
Education
Figure 1: Education (C013), in %. There are large differences concerning education of motorcyclists. Primary school level of motorcyclists has most frequently been found in Austria, Hungary, Germany and Spain. Further education level is more often in Israel, Cyprus and Netherlands than in the other countries (Figure 1).
Figure 1: Education (C013), in %. There are large differences concerning education of motorcyclists. Primary school level of motorcyclists has most frequently been found in Austria, Hungary, Germany and Spain. Further education level is more often in Israel, Cyprus and Netherlands than in the other countries (Figure 1).
Table 2: Family situation (C010) and children (C011), in %. Motorcycle type There are also large country differences concerning motorcycle type (see Figure 2). Scooters are most frequently used in Israel, Greece, Italy and Spain. The sport style is found most frequently in Serbia, Czech Rep, Sweden and Estonia. Conventional street motorcycles are most frequently used in Poland, Ireland and Austria. Enduro or offroad motorcycles will mainly be found in Cyprus, Greece, Italy and Slovenia. Touring style motorcycles are typical for Netherlands, France, Israel and Hungary. Choppers are most frequently used in Finland, Netherlands and Sweden. In general there is a high proportion of scooter riders in mediterranean countries and high proportions of conventional street machines in northern countries.
Table 2: Family situation (C010) and children (C011), in %. Motorcycle type There are also large country differences concerning motorcycle type (see Figure 2). Scooters are most frequently used in Israel, Greece, Italy and Spain. The sport style is found most frequently in Serbia, Czech Rep, Sweden and Estonia. Conventional street motorcycles are most frequently used in Poland, Ireland and Austria. Enduro or offroad motorcycles will mainly be found in Cyprus, Greece, Italy and Slovenia. Touring style motorcycles are typical for Netherlands, France, Israel and Hungary. Choppers are most frequently used in Finland, Netherlands and Sweden. In general there is a high proportion of scooter riders in mediterranean countries and high proportions of conventional street machines in northern countries.
Wscooter Bconventional street Hsport style Dtouring style enduro or offroad HBchopper
Wscooter Bconventional street Hsport style Dtouring style enduro or offroad HBchopper
There are large country differences concerning engine size (see Figure 3). Less than 126cc is most frequently used in Greece, Spain, Israel and France. 126-250 is most frequently used in Israel, Italy, Serbia and Hungary. 251-500cc is typical for Israel, Austria and Czech Rep. 501-750cc will be most frequently seen in Serbia, Estonia, Netherlands and Sweden. 751+cc is most frequently used in Finland, Sweden and Belgium. In general, smaller engine size up to 250cc is more typical for mediterranean countries. In northern countries higher engine size above 750cc is used.
There are large country differences concerning engine size (see Figure 3). Less than 126cc is most frequently used in Greece, Spain, Israel and France. 126-250 is most frequently used in Israel, Italy, Serbia and Hungary. 251-500cc is typical for Israel, Austria and Czech Rep. 501-750cc will be most frequently seen in Serbia, Estonia, Netherlands and Sweden. 751+cc is most frequently used in Finland, Sweden and Belgium. In general, smaller engine size up to 250cc is more typical for mediterranean countries. In northern countries higher engine size above 750cc is used.
As can be seen in Table 1 riders who have received a speed tickets/penalty in the past three years are more likely to have been involved in an injury accident than riders who have received no speeding tickets/speed penalty Ca 57.5; p < .001). Possibly km driven is an intervening variable in this relationship since both speed tickets and accidents are strongly associated with accidents. Table1: Injury accident involvement (yes/no) by experience with speed tickets/penalties.
As can be seen in Table 1 riders who have received a speed tickets/penalty in the past three years are more likely to have been involved in an injury accident than riders who have received no speeding tickets/speed penalty Ca 57.5; p < .001). Possibly km driven is an intervening variable in this relationship since both speed tickets and accidents are strongly associated with accidents. Table1: Injury accident involvement (yes/no) by experience with speed tickets/penalties.
m™ % of powered two wheelers ticketed for speeding in 3 years
m™ % of powered two wheelers ticketed for speeding in 3 years
Table 2: ANOVA- and chi-square of speeding tickets by age, kilometrage, gender, frequency of driving and engine size.
Table 2: ANOVA- and chi-square of speeding tickets by age, kilometrage, gender, frequency of driving and engine size.
Figure 2: shows the proportion MC in each Type group who got speeding tickets. Figure 2: MC self-reported speeding tickets by MC Type.
Figure 2: shows the proportion MC in each Type group who got speeding tickets. Figure 2: MC self-reported speeding tickets by MC Type.
Figure 3: MC and Car drivers’ reported speeding tickets by age groups. Figure 3 presents the proportion of speed-ticketed drivers in each age group, for MC and car drivers.
Figure 3: MC and Car drivers’ reported speeding tickets by age groups. Figure 3 presents the proportion of speed-ticketed drivers in each age group, for MC and car drivers.
Figure 4: MC perception and experience of speed enforcement. The majority of riders had experienced no speed checks and no speed ticketing. Of the 19% of riders who believed there is a good chance for being checked for speed, 13.3% did not get a ticket, possibly, but not necessarily, indicating the effect of general deterrence. The 5.7% who got a ticket and believed there is active speed enforcement, could be interpreted as indicating either little relationship between the two or that those who get a ticket believe more strongly in the prevalence of enforcement. However, there were more riders, 13.4%, who did get a speeding ticket yet still believed that the likelihood of speed check (prevalence of speed enforcement) is very small or nul.
Figure 4: MC perception and experience of speed enforcement. The majority of riders had experienced no speed checks and no speed ticketing. Of the 19% of riders who believed there is a good chance for being checked for speed, 13.3% did not get a ticket, possibly, but not necessarily, indicating the effect of general deterrence. The 5.7% who got a ticket and believed there is active speed enforcement, could be interpreted as indicating either little relationship between the two or that those who get a ticket believe more strongly in the prevalence of enforcement. However, there were more riders, 13.4%, who did get a speeding ticket yet still believed that the likelihood of speed check (prevalence of speed enforcement) is very small or nul.
Figure 5: Perceived speeding prevalence & belief in speeding as accident cause. Figure 5 is the scatter-plot of Country mean score on ‘perceived speeding’ with Country mean of proportion of MC drivers who consider Speeding as Always a relevant cause of MC accidents. Northern and Old EU countries dominate the lower left side of the plot, while Mediterranean or New EU countries are on the upper right side. Examination of the components of the speeding prevalence index suggests that lesser than average frequency of speeding in Built-up areas and on country roads contributed to the position of the first cluster of countries, and the opposite effect worked to put the second cluster where it is on the plot.
Figure 5: Perceived speeding prevalence & belief in speeding as accident cause. Figure 5 is the scatter-plot of Country mean score on ‘perceived speeding’ with Country mean of proportion of MC drivers who consider Speeding as Always a relevant cause of MC accidents. Northern and Old EU countries dominate the lower left side of the plot, while Mediterranean or New EU countries are on the upper right side. Examination of the components of the speeding prevalence index suggests that lesser than average frequency of speeding in Built-up areas and on country roads contributed to the position of the first cluster of countries, and the opposite effect worked to put the second cluster where it is on the plot.
Support for speeding control, by road user and age Figure 6: Support of speed control measures by MC and Car drivers, by age-group.
Support for speeding control, by road user and age Figure 6: Support of speed control measures by MC and Car drivers, by age-group.
Figure 7: Percentage of responses to the statement “Penalties for speeding offences should be much more severe”.
Figure 7: Percentage of responses to the statement “Penalties for speeding offences should be much more severe”.
Thirteen percent of motorcyclists declared they may have driven their motorcycle while being probably over the legal BAC during the previous month. The proportion of motorcyclists who declarec that they drove, at least once during the last month, after they had drunk even a small amount o: alcohol is 23%. Most of them declared that they performed this behaviour rarely though. This result i: consistent with data about alcohol prevalence among killed motorcyclists: for instance, in France, ir 2009, 35% of moped riders and 20% of motorcycle riders involved in fatal crashes had a BAC above the limit (20% for car drivers, ONISR, 2011). In the United States, 34% of moped and motorcycle fata crashes are alcohol related (NHTSA, 2006) and 27% of moped and motorcycle riders involved in a fata crash have a BAC above the legal limit of 0.8 g/ 1 (23% for car drivers). Figure 1: «Over the last month, how often have you driven a motorcycle after having drunk even a small amount of alcohol?».
Thirteen percent of motorcyclists declared they may have driven their motorcycle while being probably over the legal BAC during the previous month. The proportion of motorcyclists who declarec that they drove, at least once during the last month, after they had drunk even a small amount o: alcohol is 23%. Most of them declared that they performed this behaviour rarely though. This result i: consistent with data about alcohol prevalence among killed motorcyclists: for instance, in France, ir 2009, 35% of moped riders and 20% of motorcycle riders involved in fatal crashes had a BAC above the limit (20% for car drivers, ONISR, 2011). In the United States, 34% of moped and motorcycle fata crashes are alcohol related (NHTSA, 2006) and 27% of moped and motorcycle riders involved in a fata crash have a BAC above the legal limit of 0.8 g/ 1 (23% for car drivers). Figure 1: «Over the last month, how often have you driven a motorcycle after having drunk even a small amount of alcohol?».
Note: Red shade indicates cells superior to the total, * indicates cells for which n<15. Table 1: “Over the last month, how often have you driven motorcycle after having drunk even a small amount of alcohol?”, Percentage of answers different than “never”.
Note: Red shade indicates cells superior to the total, * indicates cells for which n<15. Table 1: “Over the last month, how often have you driven motorcycle after having drunk even a small amount of alcohol?”, Percentage of answers different than “never”.
Figure 2: «Most of your motorcycle-driving friends would drink and drive a motorcycle», by country.
Figure 2: «Most of your motorcycle-driving friends would drink and drive a motorcycle», by country.
Figure 3: «You can drink and drive if you do it carefully», by country.
Figure 3: «You can drink and drive if you do it carefully», by country.
Figure 4: «How often do you think that drinking and motorcycling is the cause of motorcyclists being involved in road accidents «, by country.
Figure 4: «How often do you think that drinking and motorcycling is the cause of motorcyclists being involved in road accidents «, by country.
Figure 5: Drink-driving mean according to the legal BAC units. Error bars indicate a 99% confidence interval. What are the drink-drive related variables?
Figure 5: Drink-driving mean according to the legal BAC units. Error bars indicate a 99% confidence interval. What are the drink-drive related variables?
Table 2: Ordinal regression on drink-drive behaviour (“even a small amount”). Note: a= the reference category of each variable. The reference category of the dependent variable is ‘never’. The ‘always’ response was absent from eastern and northern countries. ns= non significant,
Table 2: Ordinal regression on drink-drive behaviour (“even a small amount”). Note: a= the reference category of each variable. The reference category of the dependent variable is ‘never’. The ‘always’ response was absent from eastern and northern countries. ns= non significant,
Motorcycle driving under the influence of medication was reported even more scarcely in our sample than driving under the influence of alcohol (Figure 6). 81% of the motorcyclists reported that they “never” drive while taking a medication that carries a “warning: it may influence your driving ability”. Figure 6: «Have you driven while taking a medication that carries a “warning: it may influence your driving ability”», by country.
Motorcycle driving under the influence of medication was reported even more scarcely in our sample than driving under the influence of alcohol (Figure 6). 81% of the motorcyclists reported that they “never” drive while taking a medication that carries a “warning: it may influence your driving ability”. Figure 6: «Have you driven while taking a medication that carries a “warning: it may influence your driving ability”», by country.
Fatigue plays a significant role in road safety, as it impairs driver alertness and performance Fatigue-related accidents on motorways or major roadways are caused by long duration of driving. Fatigue is an often overlooked but most obvious cause to an otherwise unexplainable accident in the night-time traffic (Corfitsen, 1986, 1989). Ting et al. (2008), carried out a simulator experiment about fatigue driving and revealed that sleepiness ratings, reaction times and unstable driving performance significantly increased overtime, indicating that excessive driving time is a fatigue factor and potential! cause of fatigue-related accidents. Figure 7: «In the past 12 months while driving a motorcycle how often did you realize that you were actually too tired to drive?».
Fatigue plays a significant role in road safety, as it impairs driver alertness and performance Fatigue-related accidents on motorways or major roadways are caused by long duration of driving. Fatigue is an often overlooked but most obvious cause to an otherwise unexplainable accident in the night-time traffic (Corfitsen, 1986, 1989). Ting et al. (2008), carried out a simulator experiment about fatigue driving and revealed that sleepiness ratings, reaction times and unstable driving performance significantly increased overtime, indicating that excessive driving time is a fatigue factor and potential! cause of fatigue-related accidents. Figure 7: «In the past 12 months while driving a motorcycle how often did you realize that you were actually too tired to drive?».
Table 1: Percentage of motorcyclists that often, very often or always display the behaviour (except for giving way to pedestrians for which percentage of never, rarely and sometimes is given, cf. *). In the motorcyclist’s sample, 16% of them said often, very often or always follow the vehicle in front too closely. Relatively high frequencies of this behaviour were observed in Greece (45%) and Cyprus (46%), and in general (global sample) in those younger than under 34 years old, those driving more than 5000 km per year and those owning sports motorcycles, scooters or off-road bikes. Moreover, 83% said often, very often or always give way to pedestrians at crossings. Below average scores were however observed in those who use the motorcycle less than 3 months per year, who do not fasten the helmet properly and who drive scooters. Twenty-two percent said often, very often or always drive through traffic lights on amber. Cyprus stands out with 54%. Twenty-three percent said often, very often or always overtake when they can just make it. The highest percentages were observed
Table 1: Percentage of motorcyclists that often, very often or always display the behaviour (except for giving way to pedestrians for which percentage of never, rarely and sometimes is given, cf. *). In the motorcyclist’s sample, 16% of them said often, very often or always follow the vehicle in front too closely. Relatively high frequencies of this behaviour were observed in Greece (45%) and Cyprus (46%), and in general (global sample) in those younger than under 34 years old, those driving more than 5000 km per year and those owning sports motorcycles, scooters or off-road bikes. Moreover, 83% said often, very often or always give way to pedestrians at crossings. Below average scores were however observed in those who use the motorcycle less than 3 months per year, who do not fasten the helmet properly and who drive scooters. Twenty-two percent said often, very often or always drive through traffic lights on amber. Cyprus stands out with 54%. Twenty-three percent said often, very often or always overtake when they can just make it. The highest percentages were observed
Figure 1: Mean risky behaviour scores for all countries and for the total sample. This analysis generally confirms the ranking according to the mean percentage of frequent risky behaviour per country presented in Table 1. For the top and bottom three countries there is only one exception: according the mean risky behaviour score Germany is not in the bottom three anymore, whereas Hungary is in the third to last position in the present analysis.
Figure 1: Mean risky behaviour scores for all countries and for the total sample. This analysis generally confirms the ranking according to the mean percentage of frequent risky behaviour per country presented in Table 1. For the top and bottom three countries there is only one exception: according the mean risky behaviour score Germany is not in the bottom three anymore, whereas Hungary is in the third to last position in the present analysis.
Effect of advanced motorcycle courses on risky behaviour
Effect of advanced motorcycle courses on risky behaviour
Figure 3: Percentage of motorcyclists that completed advanced motorcycle courses. Percent of subjects that completed advanced motorcycle courses Figure 4 depicts the mean risky behaviour score as a function of completing advanced motorcycle -ourses (or not) for each of the countries and for the entire sample. In 8 out of the 19 countries (Austria, Cyprus, Czech Republic, Finland, France, Germany, Ireland and Sweden) motorcyclists who have aken an advanced training course show less risky behaviour. For Belgium, Hungary, Israel and Spain notorcyclists with an advanced training course display more frequent risky behaviour. A2 x 19 ANOVA showed however, that only the main effect of country was significant (F(18,4410)=39,p < .001). The »verall main effect of taking courses was not significant (F<1), nor was the interaction between country and taking courses (F<1). Despite of that, two countries with a more then 10 percent difference between sourse takers and non-course-takers stand out: Germany (where course takers behave less risky) and dungary (where course takers behave more risky). Despite these relative large differences, however,
Figure 3: Percentage of motorcyclists that completed advanced motorcycle courses. Percent of subjects that completed advanced motorcycle courses Figure 4 depicts the mean risky behaviour score as a function of completing advanced motorcycle -ourses (or not) for each of the countries and for the entire sample. In 8 out of the 19 countries (Austria, Cyprus, Czech Republic, Finland, France, Germany, Ireland and Sweden) motorcyclists who have aken an advanced training course show less risky behaviour. For Belgium, Hungary, Israel and Spain notorcyclists with an advanced training course display more frequent risky behaviour. A2 x 19 ANOVA showed however, that only the main effect of country was significant (F(18,4410)=39,p < .001). The »verall main effect of taking courses was not significant (F<1), nor was the interaction between country and taking courses (F<1). Despite of that, two countries with a more then 10 percent difference between sourse takers and non-course-takers stand out: Germany (where course takers behave less risky) and dungary (where course takers behave more risky). Despite these relative large differences, however,
Figure 4: Mean risky behaviour score as a function of taking advanced motorcycle courses
Figure 4: Mean risky behaviour score as a function of taking advanced motorcycle courses
Figure 5: Perceived danger of 4 types of motorcycle behaviour. Overall the differences between the 4 items are relatively small, with averages on a 1-to-4 point scale ranging from 1.71 (for overtaking on the right) to 1.99 (for overtaking on the highway respectively). The mean perceived risk of weaving on the highway (1.78) and weaving in urban areas (1.84) lay within this small range. This means that for all four behaviours the perceived danger is somewhere between very and fairly, with very little variance. The differences between the items are however consistent, which is reflected in the fact that they are - in fact - significant. Obviously, mean scores hide the exact distributions of answers over the four possible categories. The figure below gives the exact distribution. Figure 5 clearly shows that the mean scores reflect the percentages of very and fairly responses well: 53% of the respondents considered overtaking on the right as very dangerous, whereas this was 43% for weaving on a highway, 42% for weaving in urban areas and only 35% for overtaking on the highway. The order of perceived risk for the four items according to these two categories is exactly the same as the rank order according to the mean scores. This is also the case for the sum of the two opposite ends of the spectrum. Only a separate analysis of the not at all category would lead to a different conclusion, but given the small percentages these results do not weigh much on the total results.
Figure 5: Perceived danger of 4 types of motorcycle behaviour. Overall the differences between the 4 items are relatively small, with averages on a 1-to-4 point scale ranging from 1.71 (for overtaking on the right) to 1.99 (for overtaking on the highway respectively). The mean perceived risk of weaving on the highway (1.78) and weaving in urban areas (1.84) lay within this small range. This means that for all four behaviours the perceived danger is somewhere between very and fairly, with very little variance. The differences between the items are however consistent, which is reflected in the fact that they are - in fact - significant. Obviously, mean scores hide the exact distributions of answers over the four possible categories. The figure below gives the exact distribution. Figure 5 clearly shows that the mean scores reflect the percentages of very and fairly responses well: 53% of the respondents considered overtaking on the right as very dangerous, whereas this was 43% for weaving on a highway, 42% for weaving in urban areas and only 35% for overtaking on the highway. The order of perceived risk for the four items according to these two categories is exactly the same as the rank order according to the mean scores. This is also the case for the sum of the two opposite ends of the spectrum. Only a separate analysis of the not at all category would lead to a different conclusion, but given the small percentages these results do not weigh much on the total results.
Figure 6: Average risk perception for all 19 participating countries. Mean perceived risk score for 4 types of motorcycle behaviour (1=very dangerous, 2=fairly, 3=not much, 4=not at all)
Figure 6: Average risk perception for all 19 participating countries. Mean perceived risk score for 4 types of motorcycle behaviour (1=very dangerous, 2=fairly, 3=not much, 4=not at all)
Figure 7: Mean risk perception as a function of completing advanced courses. In three countries, following advanced courses was associated with a higher risk perception (Greece (t(200)= -2.86, p<.01), Hungary (t(201)= -1.29, p= .20) and Ireland (t(197)= -2.27, p<.05), where the value of course completers was below 80% of the value for the non-completers). In two countries (Israel (t(189)= 1.98, p<.05) and Spain (t(389)= 1.22, p= .22) the mean score of course completers was more than 110% of the mean score of non-completers, indicating a (tendency for a) lower perceived risk for course completers. Although some of the effects were not significant, the effect size of the significant effects shows that the association between risk perception and completing advanced courses is certainly important in some countries (Cohen’s d was for instance .58 for Greece and .37 for Israel). But since it is beyond the scope of this project to study the contents and client characteristics of the courses and course takers in each of the country, we suggest that countries with substantial differences in risk perception between course takers and the other motorcyclists look into their courses with some more attention.
Figure 7: Mean risk perception as a function of completing advanced courses. In three countries, following advanced courses was associated with a higher risk perception (Greece (t(200)= -2.86, p<.01), Hungary (t(201)= -1.29, p= .20) and Ireland (t(197)= -2.27, p<.05), where the value of course completers was below 80% of the value for the non-completers). In two countries (Israel (t(189)= 1.98, p<.05) and Spain (t(389)= 1.22, p= .22) the mean score of course completers was more than 110% of the mean score of non-completers, indicating a (tendency for a) lower perceived risk for course completers. Although some of the effects were not significant, the effect size of the significant effects shows that the association between risk perception and completing advanced courses is certainly important in some countries (Cohen’s d was for instance .58 for Greece and .37 for Israel). But since it is beyond the scope of this project to study the contents and client characteristics of the courses and course takers in each of the country, we suggest that countries with substantial differences in risk perception between course takers and the other motorcyclists look into their courses with some more attention.
Figure 8: Mean risk score by age group. Mean risk score (1=very dangerous) by age groups
Figure 8: Mean risk score by age group. Mean risk score (1=very dangerous) by age groups
A principal component analysis with Varimax rotation showed that the 11 items can be aggregated in 3 different components (the 3 factors accounted for 63% of the total variance, and the Cronbach’s Alpha of each component group confirmed the reliability of this solution). The results of this analysis are presented in Table 2.
A principal component analysis with Varimax rotation showed that the 11 items can be aggregated in 3 different components (the 3 factors accounted for 63% of the total variance, and the Cronbach’s Alpha of each component group confirmed the reliability of this solution). The results of this analysis are presented in Table 2.
Constraints like having no other means of transport or no car only concern a limited number of motorcyclists (respectively 22% and 32%). The remaining items are important for about 60% of the motorcyclists (from 48% to 69%). These concern motorcycling advantages for mobility (saving time, avoid traffic jam, limit CO2 emission and saving money) and biking feeling and spirit (acceleration and speed and biker spirit).
Constraints like having no other means of transport or no car only concern a limited number of motorcyclists (respectively 22% and 32%). The remaining items are important for about 60% of the motorcyclists (from 48% to 69%). These concern motorcycling advantages for mobility (saving time, avoid traffic jam, limit CO2 emission and saving money) and biking feeling and spirit (acceleration and speed and biker spirit).
All together the results in Table 4 allow to distinguish two opposite groups of countries, generally having opposite motives. The first group includes 5 Mediterranean countries (Greece, Israel, Cyprus, Spain and Italy), but also includes Ireland and Serbia for respectively 4 and 3 items. The second group includes 4 North and Central European countries (Finland, Sweden, Germany and Slovenia), but also includes Estonia for 3 items. The remaining countries (Hungary, Belgium, Austria, the Netherlands, the Czech Republic, Poland and France), are characterised by intermediate values for most of the motives. In the Mediterranean group motorcycle advantages for mobility are crucial motives, whereas these motives are less important for the North and Central European group. The opposite trend is observed for the importance of constraints, which are a relatively more important motive in the Mediterranean group then in the Northern European group. The opposition between both groups appeared less pronounced for motives regarding biking spirit.
All together the results in Table 4 allow to distinguish two opposite groups of countries, generally having opposite motives. The first group includes 5 Mediterranean countries (Greece, Israel, Cyprus, Spain and Italy), but also includes Ireland and Serbia for respectively 4 and 3 items. The second group includes 4 North and Central European countries (Finland, Sweden, Germany and Slovenia), but also includes Estonia for 3 items. The remaining countries (Hungary, Belgium, Austria, the Netherlands, the Czech Republic, Poland and France), are characterised by intermediate values for most of the motives. In the Mediterranean group motorcycle advantages for mobility are crucial motives, whereas these motives are less important for the North and Central European group. The opposite trend is observed for the importance of constraints, which are a relatively more important motive in the Mediterranean group then in the Northern European group. The opposition between both groups appeared less pronounced for motives regarding biking spirit.
Figure 9: Inter country differences for saving time motive.
Figure 9: Inter country differences for saving time motive.
Figure 1: Percentage of respondents who “always” wear a helmet for different road types by age-groups.
Figure 1: Percentage of respondents who “always” wear a helmet for different road types by age-groups.
Figure 2: Helmet wearing rates of motorcyclists on different road types by annual mileage (percentage “always” wearing a helmet).
Figure 2: Helmet wearing rates of motorcyclists on different road types by annual mileage (percentage “always” wearing a helmet).
Figure 3: Percentage of motorcyclists who “always” wear a helmet by engine size. The helmet-wearing rate is higher among drivers riding motorcycles with an engine size greater than 250 cc, consequently with higher performance and faster (Figure 3). Over 600 cc engine size, the helmet wearing rate is significantly higher on all road types (adj. resid. of engine size 600-1000 cc on motorways is: 4,9; main roads: 4,8, country roads: 5,0; built-up areas: 5,5). The 0-125 cc category consists mostly of scooters. In this motorcycle category the helmet wearing rate is significantly lower than the mean values (adj. resid. motorways: -4,3; main roads: -2,9, country roads: -3,0; built-up areas: -4,5). Incase of motorcycles with 126-250 cc engine size the helmet wearing rate is the lowest in all other categories (adj.resid. motorways: -5,9; main roads: -6,6, country roads: -8,9; built-up areas: -7,3).
Figure 3: Percentage of motorcyclists who “always” wear a helmet by engine size. The helmet-wearing rate is higher among drivers riding motorcycles with an engine size greater than 250 cc, consequently with higher performance and faster (Figure 3). Over 600 cc engine size, the helmet wearing rate is significantly higher on all road types (adj. resid. of engine size 600-1000 cc on motorways is: 4,9; main roads: 4,8, country roads: 5,0; built-up areas: 5,5). The 0-125 cc category consists mostly of scooters. In this motorcycle category the helmet wearing rate is significantly lower than the mean values (adj. resid. motorways: -4,3; main roads: -2,9, country roads: -3,0; built-up areas: -4,5). Incase of motorcycles with 126-250 cc engine size the helmet wearing rate is the lowest in all other categories (adj.resid. motorways: -5,9; main roads: -6,6, country roads: -8,9; built-up areas: -7,3).
Figure 4: Percentage of motorcyclists “always” wearing a helmet in the different countries by different road types.
Figure 4: Percentage of motorcyclists “always” wearing a helmet in the different countries by different road types.
Table 1: Percentage of motorcyclists by annual mileages that “never” and “sometimes” carry a passenger. The frequency “sometimes” is shown in Table 1 because the highest proportion of responses belongs to this category. The less a motorcyclist drives, the more likely it is that they never carry a passenger. The most frequent category (sometimes) that relates to the carriage of passengers supports this correlation, i.e. the frequency of passenger transport increases with the increase of the annual vehicle-kilometres driven.
Table 1: Percentage of motorcyclists by annual mileages that “never” and “sometimes” carry a passenger. The frequency “sometimes” is shown in Table 1 because the highest proportion of responses belongs to this category. The less a motorcyclist drives, the more likely it is that they never carry a passenger. The most frequent category (sometimes) that relates to the carriage of passengers supports this correlation, i.e. the frequency of passenger transport increases with the increase of the annual vehicle-kilometres driven.
Table 2: Percentage of passenger-carrying motorcyclists by age-groups that “never” carry a passenger without a helmet.
Table 2: Percentage of passenger-carrying motorcyclists by age-groups that “never” carry a passenger without a helmet.
Table 3: Percentage of passenger carry motorcyclists by annual mileages that “never” carry a passenger without a helmet. Among motorcyclists who drive 1001-5000 kilometres per year and especially over 10000 kilometres, the proportion of those who “never” carry a passenger without a helmet significantly differs. The former is higher (adj.resid. is 2,1 from the crosstabs), the latter is lower (adj.resid.: -2,5), than the average (Table 3).
Table 3: Percentage of passenger carry motorcyclists by annual mileages that “never” carry a passenger without a helmet. Among motorcyclists who drive 1001-5000 kilometres per year and especially over 10000 kilometres, the proportion of those who “never” carry a passenger without a helmet significantly differs. The former is higher (adj.resid. is 2,1 from the crosstabs), the latter is lower (adj.resid.: -2,5), than the average (Table 3).
Table 4: Percentage of motorcyclists by engine sizes that never carry a passenger without a helmet. Helmet types
Table 4: Percentage of motorcyclists by engine sizes that never carry a passenger without a helmet. Helmet types
Table 5: Percentage use of helmets’ types by motorcyclists.
Table 5: Percentage use of helmets’ types by motorcyclists.
In the questions above, there were two, to which — due to wording — most respondents gave the answer “‘very or fairly”, while in the case of the other three “not much, not at all” was the most frequent answer.
In the questions above, there were two, to which — due to wording — most respondents gave the answer “‘very or fairly”, while in the case of the other three “not much, not at all” was the most frequent answer.
Table 8: Mean of the motorcyclists’ agreement rate with the statements about helmet wearing.
Table 8: Mean of the motorcyclists’ agreement rate with the statements about helmet wearing.
Responses were transformed into mean values (Table 9) to enable effective comparison. For the two questions (‘If you drive carefully it is not really necessary to fasten helmet’ and ‘I enjoy driving without wearing a helmet’) there is a significant difference by the age-group averages (F= 3,177; p= 0,007; eta’= 0,004; F= 11,546 p= 0,000; eta= 0,013). In the event of an accident, 96.1 % of motor riders agree that helmet wearing decreases the risk of serious injury, i.e. there are many who on a cognitive level admit the benefit of helmet-wearing, while there are also relatively many who are on the opinion — especially young people under 35 (mean: from 3,27 to 3,35 below the overall mean 3,44) — that it is enjoyable to ride without a helmet (Tables 7, 8 and 9). For many motorcyclists, reason and feeling are conflicted. They recognize that the helmet protects, but they also feel that it would be more pleasant and enjoyable to ride without it. Therefore a relatively high (19,9%) percentage of those who wear a helmet do so just because it is mandatory. There are practically no differences between the attitudes of females and males; the greatest difference is that males have a much higher the rate of those who find driving without a helmet more enjoyable. Table 9: Mean of the motorcyclists’ agreement rate by age-groups with the statements about helmet wearing.
Responses were transformed into mean values (Table 9) to enable effective comparison. For the two questions (‘If you drive carefully it is not really necessary to fasten helmet’ and ‘I enjoy driving without wearing a helmet’) there is a significant difference by the age-group averages (F= 3,177; p= 0,007; eta’= 0,004; F= 11,546 p= 0,000; eta= 0,013). In the event of an accident, 96.1 % of motor riders agree that helmet wearing decreases the risk of serious injury, i.e. there are many who on a cognitive level admit the benefit of helmet-wearing, while there are also relatively many who are on the opinion — especially young people under 35 (mean: from 3,27 to 3,35 below the overall mean 3,44) — that it is enjoyable to ride without a helmet (Tables 7, 8 and 9). For many motorcyclists, reason and feeling are conflicted. They recognize that the helmet protects, but they also feel that it would be more pleasant and enjoyable to ride without it. Therefore a relatively high (19,9%) percentage of those who wear a helmet do so just because it is mandatory. There are practically no differences between the attitudes of females and males; the greatest difference is that males have a much higher the rate of those who find driving without a helmet more enjoyable. Table 9: Mean of the motorcyclists’ agreement rate by age-groups with the statements about helmet wearing.
Table 10: Mean of the motorcyclists’ agreement rate by engine size with the statements about helmet wearing. Concerning the differences between motorcyclists of various engine size, the group of 126 - 250 ce engine size (Table 10) seems to stand out, especially regarding statements “If you drive carefully it is not really necessary to fasten helmet”, “J enjoy driving without wearing a helmet” and“ only wear a helmet because it is the law”. These differences are examined further in Chapter 7 (Motorcyclists’ Profiles). On the basis of the ANOVA analysis by engine size the averages for every attitude question are significantly different.
Table 10: Mean of the motorcyclists’ agreement rate by engine size with the statements about helmet wearing. Concerning the differences between motorcyclists of various engine size, the group of 126 - 250 ce engine size (Table 10) seems to stand out, especially regarding statements “If you drive carefully it is not really necessary to fasten helmet”, “J enjoy driving without wearing a helmet” and“ only wear a helmet because it is the law”. These differences are examined further in Chapter 7 (Motorcyclists’ Profiles). On the basis of the ANOVA analysis by engine size the averages for every attitude question are significantly different.
Note: All correlations significant at the 0.01 level (2-tailed).
Note: All correlations significant at the 0.01 level (2-tailed).
Figure 5: Percentage agreement of motorcyclists by country with statements about helmet wearing (‘not much’ + ’not at all’ responses). Differences between countries (Figures 5 and 6) are substantial (particularly for anti-safety statements) as the result of diverse general safety awareness and variations in the motorcyclist population There are many who agree that in case of accident wearing a helmet decreases the risk of serious injury even in countries where the rate of those wearing always the helmet is lower (e.g. Greece, Hungary) The proportion of those who agree with the statement “I enjoy driving a motorcycle without wearing < helmet” is the highest in Italy, Greece, Cyprus, Serbia; it is lowest in Estonia, Germany, Finland, Spair — i.e. in countries where a cooler and rainier weather prevails, except Spain. The proportion of thos« agreeing with “I only wear a helmet because it is the law” is the lowest in Estonia, Slovenia, Germany and France, whereas the highest in Austria, the Czech Republic, Greece, Cyprus and Serbia.
Figure 5: Percentage agreement of motorcyclists by country with statements about helmet wearing (‘not much’ + ’not at all’ responses). Differences between countries (Figures 5 and 6) are substantial (particularly for anti-safety statements) as the result of diverse general safety awareness and variations in the motorcyclist population There are many who agree that in case of accident wearing a helmet decreases the risk of serious injury even in countries where the rate of those wearing always the helmet is lower (e.g. Greece, Hungary) The proportion of those who agree with the statement “I enjoy driving a motorcycle without wearing < helmet” is the highest in Italy, Greece, Cyprus, Serbia; it is lowest in Estonia, Germany, Finland, Spair — i.e. in countries where a cooler and rainier weather prevails, except Spain. The proportion of thos« agreeing with “I only wear a helmet because it is the law” is the lowest in Estonia, Slovenia, Germany and France, whereas the highest in Austria, the Czech Republic, Greece, Cyprus and Serbia.
Table 12: Grouping of countries by helmet wearing attitudes.more significantly disagree more significantly agree In order to examine relationships between countries and the attitude questions, cross-table analysis has been used and each country has been grouped on the basis of significant residuals. For all cases the correlation by countries was significant (p= 0,000 for all questions). According to the attitude questions
Table 12: Grouping of countries by helmet wearing attitudes.more significantly disagree more significantly agree In order to examine relationships between countries and the attitude questions, cross-table analysis has been used and each country has been grouped on the basis of significant residuals. For all cases the correlation by countries was significant (p= 0,000 for all questions). According to the attitude questions
Figure 6: Percentage agreement of motorcyclists by country with statements about helmet wearing (‘very’ + ’fairly’ responses).
Figure 6: Percentage agreement of motorcyclists by country with statements about helmet wearing (‘very’ + ’fairly’ responses).
Figure 7: Percentage cluster membership by age-group.
Figure 7: Percentage cluster membership by age-group.
Figure 8: Percentage cluster distribution by engine size (cc). Safety reluctant and safety conscious are found almost equally for engine sizes up to 250 cc. Jowever, at larger engine sizes safety reluctant are much less common falling to less than 10% while afety compliant rises to more than 40% (Figure 8).
Figure 8: Percentage cluster distribution by engine size (cc). Safety reluctant and safety conscious are found almost equally for engine sizes up to 250 cc. Jowever, at larger engine sizes safety reluctant are much less common falling to less than 10% while afety compliant rises to more than 40% (Figure 8).
Figure 9: Percentage cluster distribution by country. There are some notable differences in the distribution of the different clusters across countries (Figure 9). The safety reluctant are rare in northern countries (less than 3%), account for about a fifth of respondents in Italy and Austria, is almost the largest group for Cyprus (45%), and accounts for the majority in Greece (54%) and Serbia (67%). In total 92% of the safety conscious report that they “always” wear either a full face helmet or jet helmet compared to 87% of the safety compliant and just 45% of the safety reluctant. However, just 41% of the safety reluctant “always” fasten their helmet compared to 97% of the safety conscious and 86% of the safety compliant. Similarly, the safety reluctant are unlikely to “always” wear other safety equipment such as: technical jacket (15%), back protection (11%), technical shoes (12%). In contrast rates are relatively high for the safety conscious (66%, 45%, 51%) and to a lesser extent for the safety compliant (43%, 27%, 32%).
Figure 9: Percentage cluster distribution by country. There are some notable differences in the distribution of the different clusters across countries (Figure 9). The safety reluctant are rare in northern countries (less than 3%), account for about a fifth of respondents in Italy and Austria, is almost the largest group for Cyprus (45%), and accounts for the majority in Greece (54%) and Serbia (67%). In total 92% of the safety conscious report that they “always” wear either a full face helmet or jet helmet compared to 87% of the safety compliant and just 45% of the safety reluctant. However, just 41% of the safety reluctant “always” fasten their helmet compared to 97% of the safety conscious and 86% of the safety compliant. Similarly, the safety reluctant are unlikely to “always” wear other safety equipment such as: technical jacket (15%), back protection (11%), technical shoes (12%). In contrast rates are relatively high for the safety conscious (66%, 45%, 51%) and to a lesser extent for the safety compliant (43%, 27%, 32%).
The proportion punished is significantly highest in those countries where inclination to wear a safety helmet is the lowest: Cyprus, Serbia and Greece. No-one was punished for motorcycling without a helmet in the German and French samples (Figure 10) (y7= 341,813; df= 17; p= 0,000; adj.res.: Cyprus: 132,2; Serbia: 7,7; Greece: 7,3). Figure 10: Number of motorcyclists having received a penalty for not wearing a helmet.
The proportion punished is significantly highest in those countries where inclination to wear a safety helmet is the lowest: Cyprus, Serbia and Greece. No-one was punished for motorcycling without a helmet in the German and French samples (Figure 10) (y7= 341,813; df= 17; p= 0,000; adj.res.: Cyprus: 132,2; Serbia: 7,7; Greece: 7,3). Figure 10: Number of motorcyclists having received a penalty for not wearing a helmet.
The frequency of use of other safety devices listed in the questionnaire is presented in the table 13: In order to analyse the use of other safety equipment, the answers given to all questions about safety devices, the use of different devices were grouped into two categories (“never” use and “at least rarely or more frequently” use other safety devices) which as respondent-categories were examined by the motorcyclists’ gender, age-group, annual performance and the engine cylinder capacity of the motorcycle.
The frequency of use of other safety devices listed in the questionnaire is presented in the table 13: In order to analyse the use of other safety equipment, the answers given to all questions about safety devices, the use of different devices were grouped into two categories (“never” use and “at least rarely or more frequently” use other safety devices) which as respondent-categories were examined by the motorcyclists’ gender, age-group, annual performance and the engine cylinder capacity of the motorcycle.
Table 14: Frequency of use of other motorcycle safety devices by age-group (‘Always’+‘very often’+‘often’+‘sometimes’+‘rarely’).
Table 14: Frequency of use of other motorcycle safety devices by age-group (‘Always’+‘very often’+‘often’+‘sometimes’+‘rarely’).
(technical jacket: x?= 37,994; df= 1; p= 0,000; adj.resid.: males: 6,2; back protection equipment: x?= 97,317; df= 1; p= 0,002; adj.resid.: males:3,1; technical shoes: y?= 21,797; df=1; p= 0,000 adj.resid.: males:4,7; phone system in the helmet: y?= 10,840; df= 1; p= 0,000; adj.resid.: males:3,3). Table 15: Frequency of use of other motorcycle safety devices by gender (‘Always’+‘very often’+‘often’‘sometimes’+‘rarely’).
(technical jacket: x?= 37,994; df= 1; p= 0,000; adj.resid.: males: 6,2; back protection equipment: x?= 97,317; df= 1; p= 0,002; adj.resid.: males:3,1; technical shoes: y?= 21,797; df=1; p= 0,000 adj.resid.: males:4,7; phone system in the helmet: y?= 10,840; df= 1; p= 0,000; adj.resid.: males:3,3). Table 15: Frequency of use of other motorcycle safety devices by gender (‘Always’+‘very often’+‘often’‘sometimes’+‘rarely’).
(technical jacket: y?= 96,504; df= 3; p= 0,000; adjusted residuals: annual mileage: 0-1000 km:-8,5; 5000-10000 km: 4,4;10000km+.-5,1; back protection equipment: y?= 113,183; df= 3; p= 0,000 adj. resid.: annual mileage: 0-1000 km:-8,9; 5000-10000 km: 3,4;10000km+:6,8; technical shoes: y?= 72,755; df= 3; p= 0,000; adj. resid.: annual mileage: 0-1000 km:-7,8; 5000-10000 km: 2,7;10000km+:4,5; phone system in the helmet: y?= 72,880; df= 3; p= 0,000, adj. resid.: annual mileage: 0-1000 km:-3,0; 1000-5000 km: -5,6;5000-10000 km: 3,0;10000km+:6,9). Table 16: Frequency of use of other motorcycle safety devices by annual mileage (‘Always’+‘very often’+‘often’‘sometimes’+‘rarely’).
(technical jacket: y?= 96,504; df= 3; p= 0,000; adjusted residuals: annual mileage: 0-1000 km:-8,5; 5000-10000 km: 4,4;10000km+.-5,1; back protection equipment: y?= 113,183; df= 3; p= 0,000 adj. resid.: annual mileage: 0-1000 km:-8,9; 5000-10000 km: 3,4;10000km+:6,8; technical shoes: y?= 72,755; df= 3; p= 0,000; adj. resid.: annual mileage: 0-1000 km:-7,8; 5000-10000 km: 2,7;10000km+:4,5; phone system in the helmet: y?= 72,880; df= 3; p= 0,000, adj. resid.: annual mileage: 0-1000 km:-3,0; 1000-5000 km: -5,6;5000-10000 km: 3,0;10000km+:6,9). Table 16: Frequency of use of other motorcycle safety devices by annual mileage (‘Always’+‘very often’+‘often’‘sometimes’+‘rarely’).
(technical jacket: y?= 485,264; df= 4; p= 0,000; adjusted residuals: engine size: 0-125 cc:-18,1; 125-250 cc: -8,4;251-600 cc:4,6;601-1000 cc: 11,7; 1000+: 7,7; back protection equipment: y?= 254,953; df= 4; p= 0,000; 0-125 cc:-13,1; 125-250 cc: -6,0; 251-600 cc:3,0; 601-1000 cc: 8,8; 1000+: 5,4; technical shoes: y?= 72,755; df= 3; p= 0,000; adj. resid.: 0-125 cc:-13,1; 125-250 cc: -6,0; 251-600 cc:3,0; 601-1000 cc: 8,8; 1000+: 5,4; phone system in the helmet: y?= 15,065; df= 4; p= 0,005; adj. resid.: 0-125 cc:-2,3; 125-250 cc: 2,7). Table 17: Frequency of use of other motorcycle safety devices by motorcycle engine size (‘Always’+‘very often’+‘often’‘sometimes’+‘rarely’).
(technical jacket: y?= 485,264; df= 4; p= 0,000; adjusted residuals: engine size: 0-125 cc:-18,1; 125-250 cc: -8,4;251-600 cc:4,6;601-1000 cc: 11,7; 1000+: 7,7; back protection equipment: y?= 254,953; df= 4; p= 0,000; 0-125 cc:-13,1; 125-250 cc: -6,0; 251-600 cc:3,0; 601-1000 cc: 8,8; 1000+: 5,4; technical shoes: y?= 72,755; df= 3; p= 0,000; adj. resid.: 0-125 cc:-13,1; 125-250 cc: -6,0; 251-600 cc:3,0; 601-1000 cc: 8,8; 1000+: 5,4; phone system in the helmet: y?= 15,065; df= 4; p= 0,005; adj. resid.: 0-125 cc:-2,3; 125-250 cc: 2,7). Table 17: Frequency of use of other motorcycle safety devices by motorcycle engine size (‘Always’+‘very often’+‘often’‘sometimes’+‘rarely’).
Table 18: Frequency of use of other motorcycle safety devices by countries (‘Always’+‘very often’+‘often’‘sometimes’+‘rarely’). Each countries’ responses to the questions use of other motorcycle safety devices differ significantly by countries (technical jacket: y?= 487,865; df= 18; p= 0,000; back protection equipment: y?= 335,387; technical shoes: x?= 626,986; phone system in the helmet: x?= 401,990).
Table 18: Frequency of use of other motorcycle safety devices by countries (‘Always’+‘very often’+‘often’‘sometimes’+‘rarely’). Each countries’ responses to the questions use of other motorcycle safety devices differ significantly by countries (technical jacket: y?= 487,865; df= 18; p= 0,000; back protection equipment: y?= 335,387; technical shoes: x?= 626,986; phone system in the helmet: x?= 401,990).
Figure 1: Injury accident involvement by country for motorcyclists and car drivers (in %). 2nder and age Figure 1: Injury accident involvement by country for motorcyclists and car drivers (in %). Overall 11% of the interviewed motorcyclists have had an injury accident within the last three years. Car drivers in comparison to motorcyclists have a total involvement rate for injury accidents of 6%. Separated into countries there are significant differences in the accident involvement of the participating motorcyclists. The highest rates of accident involvement can be found for Israel (33%), followed by Cyprus (25%) and Italy (20%), the lowest rates can be found for Hungary (3%), Netherlands (4%), Germany (5%) and Poland (5%). In comparison to car drivers’ accident rates there are strong differences especially for Israel, Cyprus and Italy but also for Estonia, Sweden and France (see Figure 1).
Figure 1: Injury accident involvement by country for motorcyclists and car drivers (in %). 2nder and age Figure 1: Injury accident involvement by country for motorcyclists and car drivers (in %). Overall 11% of the interviewed motorcyclists have had an injury accident within the last three years. Car drivers in comparison to motorcyclists have a total involvement rate for injury accidents of 6%. Separated into countries there are significant differences in the accident involvement of the participating motorcyclists. The highest rates of accident involvement can be found for Israel (33%), followed by Cyprus (25%) and Italy (20%), the lowest rates can be found for Hungary (3%), Netherlands (4%), Germany (5%) and Poland (5%). In comparison to car drivers’ accident rates there are strong differences especially for Israel, Cyprus and Italy but also for Estonia, Sweden and France (see Figure 1).
Table 1: Accident involvement by gender respectively age and country (in % of gender respectively age group).
Table 1: Accident involvement by gender respectively age and country (in % of gender respectively age group).
Figure 2: Injury accident involvement by country for motorcycle type (in % of accident involved motorcyclists).
Figure 2: Injury accident involvement by country for motorcycle type (in % of accident involved motorcyclists).
Table 2: Injury accident involvement by country for motorcycle engine size (in % of accident involved motorcyclists).
Table 2: Injury accident involvement by country for motorcycle engine size (in % of accident involved motorcyclists).
For some topics it is interesting to differ between northern and southern European countries Therefore the 19 participating countries are divorced into north and south. The northern countrie: are Austria, Belgium, Czech Republic, Estonia, Finland, Germany, Ireland, Netherlands, Poland anc Sweden and the southern countries are Cyprus, France, Greece, Hungary, Israel, Italy, Serbia, Slovenis and Spain. The proportions of accident involved motorcyclists differ between these two groups. Ir mean 8% of the motorcyclists from northern Europe have been involved in an accident whereas thi: proportion is 15% for southern Europe. Figure 3: Participating countries separated into northern and southern European countries.
For some topics it is interesting to differ between northern and southern European countries Therefore the 19 participating countries are divorced into north and south. The northern countrie: are Austria, Belgium, Czech Republic, Estonia, Finland, Germany, Ireland, Netherlands, Poland anc Sweden and the southern countries are Cyprus, France, Greece, Hungary, Israel, Italy, Serbia, Slovenis and Spain. The proportions of accident involved motorcyclists differ between these two groups. Ir mean 8% of the motorcyclists from northern Europe have been involved in an accident whereas thi: proportion is 15% for southern Europe. Figure 3: Participating countries separated into northern and southern European countries.
Within the group of accident involved motorcyclists the two youngest age groups have a higher proportion than within the group of non accident involved motorcyclists (see Figure 4). Within the group of accident involved motorcyclists 18-24 year-olds have a proportion of 20% and 25-34 year- olds 35%, whereas within the group of non accident involved motorcyclists 18-24 year-olds have a proportion of 14 % and 25-34 year-olds of 26%. A t-test revealed significant differences between both groups and the calculation of the effect size cohen’s d showed a small effect of d= .32. Figure 4: Proportion of age groups for accident involved and non involved motorcyclists (in %).
Within the group of accident involved motorcyclists the two youngest age groups have a higher proportion than within the group of non accident involved motorcyclists (see Figure 4). Within the group of accident involved motorcyclists 18-24 year-olds have a proportion of 20% and 25-34 year- olds 35%, whereas within the group of non accident involved motorcyclists 18-24 year-olds have a proportion of 14 % and 25-34 year-olds of 26%. A t-test revealed significant differences between both groups and the calculation of the effect size cohen’s d showed a small effect of d= .32. Figure 4: Proportion of age groups for accident involved and non involved motorcyclists (in %).
Figure 5: Proportion of family status for accident involved and non involved motorcyclists (in %). The majority of accident involved motorcyclists is single (41%), followed by married (31%) and as married living motorcyclists (20%). In comparison to the distribution among all motorcyclists (see Chapter | “Introduction” & Figue 6.5), single and as married living motorcyclists are overrepresented in the distribution of accident involved motorcyclists.
Figure 5: Proportion of family status for accident involved and non involved motorcyclists (in %). The majority of accident involved motorcyclists is single (41%), followed by married (31%) and as married living motorcyclists (20%). In comparison to the distribution among all motorcyclists (see Chapter | “Introduction” & Figue 6.5), single and as married living motorcyclists are overrepresented in the distribution of accident involved motorcyclists.
Table 3: Behaviour when motorcycling. Accident involved compared to non involved motorcyclists by age group (often, very often and always in %, * never, rarely and sometimes in %).
Table 3: Behaviour when motorcycling. Accident involved compared to non involved motorcyclists by age group (often, very often and always in %, * never, rarely and sometimes in %).
A higher proportion of accident involved compared to non accident involved motorcyclists indicate that they often, very often or always ride in a riskier respectively more aggressive way like flashing the light or using the horn when angered or following the vehicle in front too closely (see Figure 6). The effect sizes of the mean differences lie between d= .33 and d= .46, which reflect small effects. Figure 6: Behaviour when motorcycling. Accident involved compared to non involved motorcyclists (often, very often and always in %, * never, rarely and sometimes in %).
A higher proportion of accident involved compared to non accident involved motorcyclists indicate that they often, very often or always ride in a riskier respectively more aggressive way like flashing the light or using the horn when angered or following the vehicle in front too closely (see Figure 6). The effect sizes of the mean differences lie between d= .33 and d= .46, which reflect small effects. Figure 6: Behaviour when motorcycling. Accident involved compared to non involved motorcyclists (often, very often and always in %, * never, rarely and sometimes in %).
Figure 7: Proportion of motorcyclists with a fine. Accident involved compared to non involved motorcyclists (in %). There are significant more motorcyclists who have got a ticket for any traffic violation out of the following four violations — speeding, helmet use, alcohol or medicines — within the group of the accident involved motorcyclists than in the group of non accident involved motorcyclists (see Figure 7). The effect sizes for speed ticket (d= .38), helmet ticket (d= .39) and alcohol ticket (d= .39) show a small effect when accident involved and non accident involved motorcyclists are compared.
Figure 7: Proportion of motorcyclists with a fine. Accident involved compared to non involved motorcyclists (in %). There are significant more motorcyclists who have got a ticket for any traffic violation out of the following four violations — speeding, helmet use, alcohol or medicines — within the group of the accident involved motorcyclists than in the group of non accident involved motorcyclists (see Figure 7). The effect sizes for speed ticket (d= .38), helmet ticket (d= .39) and alcohol ticket (d= .39) show a small effect when accident involved and non accident involved motorcyclists are compared.
Msouthaccident msouthnoaccident Mnorthaccident ™northno accident If the region is taken into account it comes clear that motorcyclists from Northern Europe mi often are fined for speeding than for other violations and than motorcyclists from Southern Europe countries (see Figure 8). The latter are also most often fined for speeding, but additionally they : more often fined for not wearing a helmet or riding under the influence of alcohol than North« motorcyclists. In both regions accident involved motorcyclists have more tickets for traffic violatic than non involved. Possibly there is an underestimation of speeding and alcohol violations in South« European countries because there are on average fewer checks for speeding and alcohol in this regi than in Northern European countries (ETSC)"®. So the regional difference for speeding tickets may smaller than described and the difference for alcohol tickets may be higher. There is no backgrou information about the frequency of checks for wearing a helmet and driving under the influence medicines.
Msouthaccident msouthnoaccident Mnorthaccident ™northno accident If the region is taken into account it comes clear that motorcyclists from Northern Europe mi often are fined for speeding than for other violations and than motorcyclists from Southern Europe countries (see Figure 8). The latter are also most often fined for speeding, but additionally they : more often fined for not wearing a helmet or riding under the influence of alcohol than North« motorcyclists. In both regions accident involved motorcyclists have more tickets for traffic violatic than non involved. Possibly there is an underestimation of speeding and alcohol violations in South« European countries because there are on average fewer checks for speeding and alcohol in this regi than in Northern European countries (ETSC)"®. So the regional difference for speeding tickets may smaller than described and the difference for alcohol tickets may be higher. There is no backgrou information about the frequency of checks for wearing a helmet and driving under the influence medicines.
There is a higher proportion of motorcyclists who never or rarely drive after drinking few alcohol or more alcohol than permitted within the non accident involved group than in the accident involved group (see Figure 9). This fact mirrors the result of question MC 14 (see Figure 10): A higher percentage within the non accident involved group compared to the accident involved group has the opinion that the legal alcohol limit should be restricted to zero. For all three questions there are medium effect sizes (d= .46, .37 and .37) for the mean score differences between the groups. Figure 9: Driving with few alcohol (MC11) and Driving with more alcohol than permitted (MC12). Accident involved compared to non involved motorcyclists (in %).
There is a higher proportion of motorcyclists who never or rarely drive after drinking few alcohol or more alcohol than permitted within the non accident involved group than in the accident involved group (see Figure 9). This fact mirrors the result of question MC 14 (see Figure 10): A higher percentage within the non accident involved group compared to the accident involved group has the opinion that the legal alcohol limit should be restricted to zero. For all three questions there are medium effect sizes (d= .46, .37 and .37) for the mean score differences between the groups. Figure 9: Driving with few alcohol (MC11) and Driving with more alcohol than permitted (MC12). Accident involved compared to non involved motorcyclists (in %).
Figure 10: Attitude towards legal alcohol limit (MC14). Accident involved compared to non involved motorcyclists (in %).
Figure 10: Attitude towards legal alcohol limit (MC14). Accident involved compared to non involved motorcyclists (in %).
Figure 12 shows that for Southern European countries the difference between accident involvec and non involved motorcyclists is bigger than for northern European countries. Accident involvec motorcyclists from southern European countries have the highest rates on all questions and they especially enjoy riding without a helmet, wear a helmet only because it is the law and their friends more often wear no helmet.
Figure 12 shows that for Southern European countries the difference between accident involvec and non involved motorcyclists is bigger than for northern European countries. Accident involvec motorcyclists from southern European countries have the highest rates on all questions and they especially enjoy riding without a helmet, wear a helmet only because it is the law and their friends more often wear no helmet.
Accident involved and non accident involved motorcyclists differ in their perception of risk in specific situations (see Figure 13). Accident involved motorcyclists perceive specific situations like “overtaking on the right”, “weaving between lanes on highways” and “weaving between lanes on urban streets” less risky than non accident involved motorcyclists. But those differences between the two groups are very small (d between .11 and .16) so that it is only a tendency. Figure 13: Risk perception. Accident involved compared to non involved motorcyclists (not much and not at all in %).
Accident involved and non accident involved motorcyclists differ in their perception of risk in specific situations (see Figure 13). Accident involved motorcyclists perceive specific situations like “overtaking on the right”, “weaving between lanes on highways” and “weaving between lanes on urban streets” less risky than non accident involved motorcyclists. But those differences between the two groups are very small (d between .11 and .16) so that it is only a tendency. Figure 13: Risk perception. Accident involved compared to non involved motorcyclists (not much and not at all in %).
Motorcyclists have different motives for using their motorcycle depending on the purpose they ride it for (see Figure 14). Asked for their reasons to ride a motorcycle a significant higher proportion of accident involved motorcyclists indicate that they ride a motorcycle because they enjoy acceleration and high speed than the proportion of non accident involved motorcyclists (d= .17). Again this is only a small effect. The two groups don’t differ in their motivation by the feeling of freedom, pleasure and biker spirit. Figure 14: Motives for riding a motorcycle (Extramotives). Accident involved compared to non involved motorcyclists (very and fairly in %).
Motorcyclists have different motives for using their motorcycle depending on the purpose they ride it for (see Figure 14). Asked for their reasons to ride a motorcycle a significant higher proportion of accident involved motorcyclists indicate that they ride a motorcycle because they enjoy acceleration and high speed than the proportion of non accident involved motorcyclists (d= .17). Again this is only a small effect. The two groups don’t differ in their motivation by the feeling of freedom, pleasure and biker spirit. Figure 14: Motives for riding a motorcycle (Extramotives). Accident involved compared to non involved motorcyclists (very and fairly in %).
The comparison separated for Northern and Southern European countries reveals that motorcyclists from northern European countries are more motivated to ride a motorcycle because of the so called extra motives (see Figure 15). Only for motorcyclists from Northern countries there are higher rates for the motives freedom and biker spirit for accident involved than non accident involved motorcyclists.
The comparison separated for Northern and Southern European countries reveals that motorcyclists from northern European countries are more motivated to ride a motorcycle because of the so called extra motives (see Figure 15). Only for motorcyclists from Northern countries there are higher rates for the motives freedom and biker spirit for accident involved than non accident involved motorcyclists.
Table 1: Principal Component Analysis combining MC25 and MC24 items. 17 - Results previously presented in Chapter 4 concerning motivations for driving a motorbike as assessed through MC24 items (based on a Principal Component Analysis with Varimax rotation) have shown that these 11 motivations can be aggregated in 3 differ- ent components (the rotated factors accounted for 63% of the total variance, and the different values of Cronbach’s Alpha statistics confirmed the reliability of these 3 groups of items). The first component deals with motorcycling advantages for mobility (compared with a car). Five items loaded on to this component 1, which accounted for 30.6% of the variance: avoiding traffic jam, saving time, easiness for parking, cheaper mean of transport and lastly, motivation to reduce CO2 pollution. The second sub-set of motivations (component 2, accounted for 20.6% of the variance) is related with biking feeling and spirit and integrates 4 items: riding pleasure, acceleration and speed, biker spirit, and freedom feeling). At last, a sub-set of motivations (accounted for 11.7% of the variance) is related with imposed constraints and includes 2 items: not having any car or other choice for mobility.
Table 1: Principal Component Analysis combining MC25 and MC24 items. 17 - Results previously presented in Chapter 4 concerning motivations for driving a motorbike as assessed through MC24 items (based on a Principal Component Analysis with Varimax rotation) have shown that these 11 motivations can be aggregated in 3 differ- ent components (the rotated factors accounted for 63% of the total variance, and the different values of Cronbach’s Alpha statistics confirmed the reliability of these 3 groups of items). The first component deals with motorcycling advantages for mobility (compared with a car). Five items loaded on to this component 1, which accounted for 30.6% of the variance: avoiding traffic jam, saving time, easiness for parking, cheaper mean of transport and lastly, motivation to reduce CO2 pollution. The second sub-set of motivations (component 2, accounted for 20.6% of the variance) is related with biking feeling and spirit and integrates 4 items: riding pleasure, acceleration and speed, biker spirit, and freedom feeling). At last, a sub-set of motivations (accounted for 11.7% of the variance) is related with imposed constraints and includes 2 items: not having any car or other choice for mobility.
Table 2 shows the results of the classification of the motorcyclists in the sample into the four profile subgroups. The size composition of the subgroups represents the total SARTRE 4 sample and not necessarily a particular country. In the next sections we compare the four profile sub-groups in terms of rider and motorcycle characteristics, training and experience.
Table 2 shows the results of the classification of the motorcyclists in the sample into the four profile subgroups. The size composition of the subgroups represents the total SARTRE 4 sample and not necessarily a particular country. In the next sections we compare the four profile sub-groups in terms of rider and motorcycle characteristics, training and experience.
*Legend: in Significant Differences cells, C means Commuter, R means Rambler, S means Sport Riders and T means Traveller
*Legend: in Significant Differences cells, C means Commuter, R means Rambler, S means Sport Riders and T means Traveller
On the other side, Scooters are the typical motorbike used by Commuters (used by 42% of them against by less of 9% of the members of the other groups) and sport style motorbikes are mainly driven by Sport Riders (45% of them). Moreover, a highest percentage of Ramblers and Travellers - in comparison with Commuters and Sport Riders - ride Touring motorbikes and Choppers. Lastly, Enduros (Off-road bikes) are proportionally more associated with the group of Travellers.
On the other side, Scooters are the typical motorbike used by Commuters (used by 42% of them against by less of 9% of the members of the other groups) and sport style motorbikes are mainly driven by Sport Riders (45% of them). Moreover, a highest percentage of Ramblers and Travellers - in comparison with Commuters and Sport Riders - ride Touring motorbikes and Choppers. Lastly, Enduros (Off-road bikes) are proportionally more associated with the group of Travellers.
Table 5 shows that Commuters ride on less powerful motorbikes than others groups of motorcyclists (400cc as mean value, against 659-735 cc). This mean value is indeed due to the highest proportion (about 70%) of 125cc and 126-500cc motorbikes used in the group of Commuters. The other three groups clearly ride larger motorcycles.
Table 5 shows that Commuters ride on less powerful motorbikes than others groups of motorcyclists (400cc as mean value, against 659-735 cc). This mean value is indeed due to the highest proportion (about 70%) of 125cc and 126-500cc motorbikes used in the group of Commuters. The other three groups clearly ride larger motorcycles.
Average number of months a motorbike is used per year Table 6: Participants having completed advanced motorcycle skill courses.
Average number of months a motorbike is used per year Table 6: Participants having completed advanced motorcycle skill courses.
Synthetically, it appears that Commuters use the MC primarily for daily mobility to work, school, or other regular functions, so they use the machine all year round. Ramblers, on the other hand, ride bikes primarily for pleasure, so there is an element of selection and choice in their riding, depending on having leisure time and appropriate weather and other conditions, all which serve to limit the amount of riding. Table 7: Number of months/year motorcycle is used, per profiles.
Synthetically, it appears that Commuters use the MC primarily for daily mobility to work, school, or other regular functions, so they use the machine all year round. Ramblers, on the other hand, ride bikes primarily for pleasure, so there is an element of selection and choice in their riding, depending on having leisure time and appropriate weather and other conditions, all which serve to limit the amount of riding. Table 7: Number of months/year motorcycle is used, per profiles.
Synthetical to 2 categories MC24e, MC24 ly, similar significant differences between the 4 groups appear for the 7 items corresponding of motivations: motorcycling advantages for mobility (i.e. items MC24a, MC24c, MC24d. j) and imposed Constraint (MC24h, MC24i). These 7 types of motivations for using 4 motorbike are significantly more important for the group of Commuters and, by contrast, significantly less important for t he group of Ramblers. Groups of Travellers and Sport Riders have in between opinions. Opposite results are found for the 4 items corresponding to “biking feeling and spirit’ motivations (MC24b, MC24f, MC24g, MC24k). For this last set of motivations, Commuters’ answers systematically correspond to t! he lowest values, against max values collected among Ramblers for Pleasure (intermediary values for Sport Riders and Traveller), or against Sport Riders - sometimes with Travellers and/o1 Ramblers - for Acceleration, Freedom feeling and Biking Spirit. Table 8: Motivations for using a motorbike per profiles.
Synthetical to 2 categories MC24e, MC24 ly, similar significant differences between the 4 groups appear for the 7 items corresponding of motivations: motorcycling advantages for mobility (i.e. items MC24a, MC24c, MC24d. j) and imposed Constraint (MC24h, MC24i). These 7 types of motivations for using 4 motorbike are significantly more important for the group of Commuters and, by contrast, significantly less important for t he group of Ramblers. Groups of Travellers and Sport Riders have in between opinions. Opposite results are found for the 4 items corresponding to “biking feeling and spirit’ motivations (MC24b, MC24f, MC24g, MC24k). For this last set of motivations, Commuters’ answers systematically correspond to t! he lowest values, against max values collected among Ramblers for Pleasure (intermediary values for Sport Riders and Traveller), or against Sport Riders - sometimes with Travellers and/o1 Ramblers - for Acceleration, Freedom feeling and Biking Spirit. Table 8: Motivations for using a motorbike per profiles.
*(% answering that other MC Often to Always brake speed limit; % of positive answers for speed check/ticket). Table 9: Inter-profiles comparisons concerning others MC speeding attitudes and practices*.
*(% answering that other MC Often to Always brake speed limit; % of positive answers for speed check/ticket). Table 9: Inter-profiles comparisons concerning others MC speeding attitudes and practices*.
*(Helmet wearing: % saying Often to Always; Opinions: % saying agree Very to Fairly)
*(Helmet wearing: % saying Often to Always; Opinions: % saying agree Very to Fairly)
*(% answering Often to Always, Very often to Fairly, or got a ticket).
*(% answering Often to Always, Very often to Fairly, or got a ticket).
Table 13 presents the mean group values (and MANOVA) of proportion of riders who frequently committed the actions and the corresponding proportion of riders who deemed such actions as Very or Fairly dangerous. Sports riders consistently and significantly reported larger proportion of riders committing risky actions (‘yielding to pedestrian’ should be read as a reversed scale) compared to riders in other groups, and particularly compared to the Ramblers group. In a similar way Sport Riders assess as less dangerous, than the other groups, motorcycling maneuvers such as weaving (in urban area or on motorway) or overtaking cars between lines or on the right.
Table 13 presents the mean group values (and MANOVA) of proportion of riders who frequently committed the actions and the corresponding proportion of riders who deemed such actions as Very or Fairly dangerous. Sports riders consistently and significantly reported larger proportion of riders committing risky actions (‘yielding to pedestrian’ should be read as a reversed scale) compared to riders in other groups, and particularly compared to the Ramblers group. In a similar way Sport Riders assess as less dangerous, than the other groups, motorcycling maneuvers such as weaving (in urban area or on motorway) or overtaking cars between lines or on the right.
Discussion concerning Motorcyclists’ profiles By considering the results presented, it appears that the 4 profiles of motorcyclists identified through this SARTRE 4 survey are very contrasted according to their motivations for driving a motorbike and to their attitude towards risk and risk taking when motorcycling. Indeed, if they are in means globally comparable concerning their general characteristics like Age (39 years old more or less 3 years) and Motorcycling Experience (from 4500 to 9500 km/year and around 14 years of riding practice), they are by contrast very different regarding other dimensions investigated in this survey. The following section gives a synthetic overview of each one of these 4 profiles. This result must be however considered with caution, according to the fact that Ramblers covered a significant lowest number of kilometres per year with a motorbike, comparing with the 3 other groups (only 4652 km/year, against 7588 for Commuters, 8225 for Sport Riders and 9472 for Travellers; see Table 3) and — more significant - ride their bike during a shortest period (6-months per year, against 8 months for Sport Riders and Travellers and 10 months for Commuters; see Table 7), potentially excluding motorcycling practice under bad weather conditions. If the rate of accident per 100.000 km covered seems to confirm a lowest accident risk for Ramblers (1.72, against a risk of 2.3 for Sport Riders or Travellers and of 2.63 for Commuters), this MC group probably mainly use their motorbike during the summer period and/or under better weather conditions than others groups. By contrast, Commuters ride their bike 10 months per year including autumn and winter periods (in-between values were collected among Sport Riders and Travellers). Accident risk differences observed between groups could be thus potentially explained by such differences in riding practices (or not) under bad weather conditions.
Discussion concerning Motorcyclists’ profiles By considering the results presented, it appears that the 4 profiles of motorcyclists identified through this SARTRE 4 survey are very contrasted according to their motivations for driving a motorbike and to their attitude towards risk and risk taking when motorcycling. Indeed, if they are in means globally comparable concerning their general characteristics like Age (39 years old more or less 3 years) and Motorcycling Experience (from 4500 to 9500 km/year and around 14 years of riding practice), they are by contrast very different regarding other dimensions investigated in this survey. The following section gives a synthetic overview of each one of these 4 profiles. This result must be however considered with caution, according to the fact that Ramblers covered a significant lowest number of kilometres per year with a motorbike, comparing with the 3 other groups (only 4652 km/year, against 7588 for Commuters, 8225 for Sport Riders and 9472 for Travellers; see Table 3) and — more significant - ride their bike during a shortest period (6-months per year, against 8 months for Sport Riders and Travellers and 10 months for Commuters; see Table 7), potentially excluding motorcycling practice under bad weather conditions. If the rate of accident per 100.000 km covered seems to confirm a lowest accident risk for Ramblers (1.72, against a risk of 2.3 for Sport Riders or Travellers and of 2.63 for Commuters), this MC group probably mainly use their motorbike during the summer period and/or under better weather conditions than others groups. By contrast, Commuters ride their bike 10 months per year including autumn and winter periods (in-between values were collected among Sport Riders and Travellers). Accident risk differences observed between groups could be thus potentially explained by such differences in riding practices (or not) under bad weather conditions.
Figure 1: Percentages of each profile per country.
Figure 1: Percentages of each profile per country.
In 2000, 9,476 pedestrians were killed in road traffic accidents in the EU-19'*, but by 2009 that ad reduced to 6,233, accounting for 20% of road traffic fatalities. In The Netherlands, there are just .4 pedestrian fatalities per million inhabitants, but in Romania and Poland the rate is about 15 times igher at 49. Similarly, pedestrian fatalities account for just 10% of all road traffic fatalities in the Netherlands, but in Romania, Poland and Latvia that percentage rises to more than 30%. The largest Troup in pedestrian fatalities is those aged above 64 years of age (with a peak between 75 and 79 years f age) making up more than half of all pedestrian fatalities in France, Germany, Italy and Slovenia. .ess than one quarter of all fatalities were female, but more than one third of pedestrian fatalities were emale (ERSO, 2009a). Respondents were classified as Other Road Users (ORU) if they indicated that either “Driving < car’ or “Riding a motorcycle > 50 cc” was not their “most frequent mode of transport during the las 12 months”. There were five options available with varying popularity (Table 1). Walking was mos popular, followed by public transport, being a car passenger, cycling and — least popular — using < moped. In this section we are most concerned with the three most popular options: walking, cycling and using public transport. It is interesting to note that 30% of our respondents reported using all thre« of these modes on a typical day. There are separate chapters dedicated to being a pedestrian and being a cyclist as these are two modes of transport that are being promoted for their environmental and healtl benefits. However, both of these modes leave the user vulnerable to the consequences of a road traffic accident due to the intrinsic vulnerability of the participant. In contrast, it is possible to add safety devices to cars and motor cycles — such as Anti-lock braking system (ABS) and air bags — but it is no feasible for pedestrians and difficult for cyclists beyond the use of a cycling helmet.
In 2000, 9,476 pedestrians were killed in road traffic accidents in the EU-19'*, but by 2009 that ad reduced to 6,233, accounting for 20% of road traffic fatalities. In The Netherlands, there are just .4 pedestrian fatalities per million inhabitants, but in Romania and Poland the rate is about 15 times igher at 49. Similarly, pedestrian fatalities account for just 10% of all road traffic fatalities in the Netherlands, but in Romania, Poland and Latvia that percentage rises to more than 30%. The largest Troup in pedestrian fatalities is those aged above 64 years of age (with a peak between 75 and 79 years f age) making up more than half of all pedestrian fatalities in France, Germany, Italy and Slovenia. .ess than one quarter of all fatalities were female, but more than one third of pedestrian fatalities were emale (ERSO, 2009a). Respondents were classified as Other Road Users (ORU) if they indicated that either “Driving < car’ or “Riding a motorcycle > 50 cc” was not their “most frequent mode of transport during the las 12 months”. There were five options available with varying popularity (Table 1). Walking was mos popular, followed by public transport, being a car passenger, cycling and — least popular — using < moped. In this section we are most concerned with the three most popular options: walking, cycling and using public transport. It is interesting to note that 30% of our respondents reported using all thre« of these modes on a typical day. There are separate chapters dedicated to being a pedestrian and being a cyclist as these are two modes of transport that are being promoted for their environmental and healtl benefits. However, both of these modes leave the user vulnerable to the consequences of a road traffic accident due to the intrinsic vulnerability of the participant. In contrast, it is possible to add safety devices to cars and motor cycles — such as Anti-lock braking system (ABS) and air bags — but it is no feasible for pedestrians and difficult for cyclists beyond the use of a cycling helmet.
These inter-country variations are also seen for the different age groups for those reporting engaging in non-zero distances of walking (Figure 1), cycling (Figure 2) and using public transport (Figure 3). In Cyprus and Sweden, the majority of those walking were in the youngest age group (18 — 24 years of age) whereas in most other countries that age group accounted for less than 20% of those walking. In contrast, for Estonia, Greece, The Netherlands, Serbia and Sweden, less than 10% of those walking were aged 65 years of age or older, but Finland, Germany, Hungary and Italy all had more than 30% of those walking aged over 65 years of age. Figure 1: Percentage of age groups by country for those reporting walking on a typical day.
These inter-country variations are also seen for the different age groups for those reporting engaging in non-zero distances of walking (Figure 1), cycling (Figure 2) and using public transport (Figure 3). In Cyprus and Sweden, the majority of those walking were in the youngest age group (18 — 24 years of age) whereas in most other countries that age group accounted for less than 20% of those walking. In contrast, for Estonia, Greece, The Netherlands, Serbia and Sweden, less than 10% of those walking were aged 65 years of age or older, but Finland, Germany, Hungary and Italy all had more than 30% of those walking aged over 65 years of age. Figure 1: Percentage of age groups by country for those reporting walking on a typical day.
Table 2: Percentage of respondents who are female who indicated non-zero amounts of usage of each transport mode in a typical day. In the next chapter, the motivations of Other Road Users are considered. In particular, the overlap of different modes by individuals is recognised. Indeed, it is not uncommon for road users to make use of a variety of different modes in the same day. Although, it is convenient to talk about car drivers and pedestrians as separate groups, the reality is that many users are both, but at different times. Therefore, cluster analysis is used to identify different types of road users based on a variety of variables. In the dedicated chapters that follow, pedestrians were identified as those respondents who reported non-zero daily walking distance travelled whereas cyclists were identified as those respondents who reported cycling, on average, one or more kilometres per day. In these two chapters, travelling style and satisfaction with the road environment and other users are considered for pedestrians and safety compliance and satisfaction with other road users are considered for cyclists. In addition, cluster analysis was used to identify different kinds of pedestrians.
Table 2: Percentage of respondents who are female who indicated non-zero amounts of usage of each transport mode in a typical day. In the next chapter, the motivations of Other Road Users are considered. In particular, the overlap of different modes by individuals is recognised. Indeed, it is not uncommon for road users to make use of a variety of different modes in the same day. Although, it is convenient to talk about car drivers and pedestrians as separate groups, the reality is that many users are both, but at different times. Therefore, cluster analysis is used to identify different types of road users based on a variety of variables. In the dedicated chapters that follow, pedestrians were identified as those respondents who reported non-zero daily walking distance travelled whereas cyclists were identified as those respondents who reported cycling, on average, one or more kilometres per day. In these two chapters, travelling style and satisfaction with the road environment and other users are considered for pedestrians and safety compliance and satisfaction with other road users are considered for cyclists. In addition, cluster analysis was used to identify different kinds of pedestrians.
Examining the statistics for fatalities of ‘other road users’ (pedestrians, cyclists, car passengeé oped drivers) in the participating countries reveals a very diverse picture. In some countries | talities of the other road user group count for more than half of total road fatalities (Poland, Estor ungary and the Netherlands). On the other hand there are countries in which this category of road u >counts for less than one third of all road fatalities (Cyprus, Sweden, Ireland and Serbia). “Passenge the largest group within other road users’ fatalities for ten countries, but in five countries there wi 9 data available for this category. Pedestrians are the other group with a high number of fataliti counting for the highest proportion in Israel with 33% of total road fatalities followed by Pole ith 32%. The share of fatalities among cyclists is highest in the Netherlands, where cycling is vi ypular. All these results have to be interpreted with caution as they depend very much on the degree se of the various means of transport and the diverse mobility habits in the participating countries ( igure 1). However, comparable exposure data regarding ‘other road users’ are not available in m articipating countries.
Examining the statistics for fatalities of ‘other road users’ (pedestrians, cyclists, car passengeé oped drivers) in the participating countries reveals a very diverse picture. In some countries | talities of the other road user group count for more than half of total road fatalities (Poland, Estor ungary and the Netherlands). On the other hand there are countries in which this category of road u >counts for less than one third of all road fatalities (Cyprus, Sweden, Ireland and Serbia). “Passenge the largest group within other road users’ fatalities for ten countries, but in five countries there wi 9 data available for this category. Pedestrians are the other group with a high number of fataliti counting for the highest proportion in Israel with 33% of total road fatalities followed by Pole ith 32%. The share of fatalities among cyclists is highest in the Netherlands, where cycling is vi ypular. All these results have to be interpreted with caution as they depend very much on the degree se of the various means of transport and the diverse mobility habits in the participating countries ( igure 1). However, comparable exposure data regarding ‘other road users’ are not available in m articipating countries.
Figure 2: Reasons for walking/cycling/using public transport (all countries). Most of the respondents agreed to the category ‘fairly’ instead of ‘very’ except for ‘financial reasons’ and ‘driving license withdrawal’ in which the proportions of these two categories were about the same. ‘Driving license withdrawal/ban’ and ‘fear of driving’ were the least agreed reasons with most answers falling into the category ‘agreed not at all’ (74% vs. 45%).
Figure 2: Reasons for walking/cycling/using public transport (all countries). Most of the respondents agreed to the category ‘fairly’ instead of ‘very’ except for ‘financial reasons’ and ‘driving license withdrawal’ in which the proportions of these two categories were about the same. ‘Driving license withdrawal/ban’ and ‘fear of driving’ were the least agreed reasons with most answers falling into the category ‘agreed not at all’ (74% vs. 45%).
Country comparison regarding the motivator ‘environment reasons’ shows similar results as for the motivator ‘health’: Respondents in Estonia and Sweden named environment reasons as a strong motivator for using alternative means of transport in these countries (72% vs. 71% agree ‘very’ or ‘fairly’). Again, at the other end of the list are Serbia and Ireland (‘very’ + ‘fairly’ agree below 25%)- together with Poland — for whom ‘environment’ is not an important factor.
Country comparison regarding the motivator ‘environment reasons’ shows similar results as for the motivator ‘health’: Respondents in Estonia and Sweden named environment reasons as a strong motivator for using alternative means of transport in these countries (72% vs. 71% agree ‘very’ or ‘fairly’). Again, at the other end of the list are Serbia and Ireland (‘very’ + ‘fairly’ agree below 25%)- together with Poland — for whom ‘environment’ is not an important factor.
Figure 4: Reasons for walking/cycling/using public transport per country: No necessity, fear and physical exercise.
Figure 4: Reasons for walking/cycling/using public transport per country: No necessity, fear and physical exercise.
Importance of motivation factor above mean: Table 2: Cluster comparison regarding motivations variable/ORU01; means of clusters compared to total.
Importance of motivation factor above mean: Table 2: Cluster comparison regarding motivations variable/ORU01; means of clusters compared to total.
Figure 1: C003. How safe do you think the roads are in our country to travel on?
Figure 1: C003. How safe do you think the roads are in our country to travel on?
CO06a-How much would you be in favour of using speed limit devices in cars? Figure 2: CO0Ga. How much would you be in favour of using speed limit devices in cars?
CO06a-How much would you be in favour of using speed limit devices in cars? Figure 2: CO0Ga. How much would you be in favour of using speed limit devices in cars?
ORUO3b-As a pedestrian how often do you cross at places other than the pedestrian crossing? Figure 4: ORUO3b. As a pedestrian, how often do you cross at places other than the pedestrian crossing?
ORUO3b-As a pedestrian how often do you cross at places other than the pedestrian crossing? Figure 4: ORUO3b. As a pedestrian, how often do you cross at places other than the pedestrian crossing?
ORU04 town size factor
ORU04 town size factor
Figure 7: ORUO4. As a pedestrian, thinking about the area in which you walk on, how satisfied are you with the following?
Figure 7: ORUO4. As a pedestrian, thinking about the area in which you walk on, how satisfied are you with the following?
ORU10a,c town size factor The perception that one can drink alcohol and walk if one does it carefully is increases significantly when the size of the municipality increases. Similarly, the perception that walking drunk increases the risk of an accident with another road user is slightly reduced when town size increases.
ORU10a,c town size factor The perception that one can drink alcohol and walk if one does it carefully is increases significantly when the size of the municipality increases. Similarly, the perception that walking drunk increases the risk of an accident with another road user is slightly reduced when town size increases.
Figure 9: ORUO8 by gender. When travelling in general, as a pedestrian, how often do you get annoyed by... Female pedestrians seem to get annoyed about other road users more often than male pedestrians (See Figure 9). However, the shares of respondents getting annoyed ‘always’ or ‘very often’ are quite small; less than 12% for ‘very often’ and less than 4 % for ‘always’. The differences between the gender groups were statistically significant except for getting annoyed about cyclists. On country level, some statistically significant differences were found:
Figure 9: ORUO8 by gender. When travelling in general, as a pedestrian, how often do you get annoyed by... Female pedestrians seem to get annoyed about other road users more often than male pedestrians (See Figure 9). However, the shares of respondents getting annoyed ‘always’ or ‘very often’ are quite small; less than 12% for ‘very often’ and less than 4 % for ‘always’. The differences between the gender groups were statistically significant except for getting annoyed about cyclists. On country level, some statistically significant differences were found:
Figure 10: ORUO8 by age. When travelling in general, as a pedestrian, how often do you...(never/rarely). ABLlidtin « kes amas anmel ~amaneam
Figure 10: ORUO8 by age. When travelling in general, as a pedestrian, how often do you...(never/rarely). ABLlidtin « kes amas anmel ~amaneam
Figure 11: Summary of cluster analysis for pedestrian types with respect to travel habits. The four types resulting from the cluster analysis are present in various proportions in the articipating countries. Type 1 (Average distance traveller, short distance pedestrian and user of public transport) covers 44.5% of the pedestrian population. In Belgium type 1| is represented significantly higher, in Israel and in Hungary by trend higher than on average. In the Netherlands, Ireland and Sweden this type is by trend lower than the mean of all countries.
Figure 11: Summary of cluster analysis for pedestrian types with respect to travel habits. The four types resulting from the cluster analysis are present in various proportions in the articipating countries. Type 1 (Average distance traveller, short distance pedestrian and user of public transport) covers 44.5% of the pedestrian population. In Belgium type 1| is represented significantly higher, in Israel and in Hungary by trend higher than on average. In the Netherlands, Ireland and Sweden this type is by trend lower than the mean of all countries.
Table 2: Component 1 loadings: “satisfaction with the pedestrian environment”. Component 2: The second component (Table 3) is correlated with the variables which concern the acceptance of various penalties such for inappropriate driver behaviour. It can be labelled as “Attitude towards penalties”. The variables involved indicate low agreement with penalties regarding speeding, drink-driving, restraint, helmet and handheld phone use.
Table 2: Component 1 loadings: “satisfaction with the pedestrian environment”. Component 2: The second component (Table 3) is correlated with the variables which concern the acceptance of various penalties such for inappropriate driver behaviour. It can be labelled as “Attitude towards penalties”. The variables involved indicate low agreement with penalties regarding speeding, drink-driving, restraint, helmet and handheld phone use.
Table 3: Component 2 loadings: “Attitude towards penalties”.
Table 3: Component 2 loadings: “Attitude towards penalties”.
Overall, the 3 clusters can be summarized as follows: Cluster 1 includes pedestrians with positive attitudes and positive behaviour, while Cluster 2 includes pedestrians with negative attitudes and negative behaviour. Cluster 3 includes pedestrians with positive behaviour, but mixed attitudes, as they agree with some types of measures but disagree with others. More specifically:
Overall, the 3 clusters can be summarized as follows: Cluster 1 includes pedestrians with positive attitudes and positive behaviour, while Cluster 2 includes pedestrians with negative attitudes and negative behaviour. Cluster 3 includes pedestrians with positive behaviour, but mixed attitudes, as they agree with some types of measures but disagree with others. More specifically:
Thirty-seven percent of male pedestrians are in Cluster 1, which suggests that they have positive attitudes and positive behaviour, while 38% has negative attitudes and negative behaviour. Only 25% has mixed attitudes. Almost half of females (48%) have positive attitudes and positive behaviour while 25% and 27% are assigned to the other two clusters.
Thirty-seven percent of male pedestrians are in Cluster 1, which suggests that they have positive attitudes and positive behaviour, while 38% has negative attitudes and negative behaviour. Only 25% has mixed attitudes. Almost half of females (48%) have positive attitudes and positive behaviour while 25% and 27% are assigned to the other two clusters.
In Figure | both the percentage of cycling other road users and the average number of kilometres driven per day per cycling other road user in each country are depicted for each of the participating countries. The horizontal axis gives the percentage of cyclists in the other road users group. On average, only about 34 percent of all European other road users cycle. The vertical axis gives the average number of kilometres driven per cyclist for each of the countries. On average, a European cycling other road user cycles about seven kilometres per day. As expected countries with a tradition in cycling like the Netherlands, Germany and Finland have the highest percentage of cyclists in the other road users group. Greece, Cyprus and Spain have the lowest percentages. The percentage of cycling other road users is unrelated to the average number of kilometres driven per cyclist per day (r= 0.00). Figure 1: Percentage of cycling other road users and average number of kilometres cycled per cycling other road user per day per country.
In Figure | both the percentage of cycling other road users and the average number of kilometres driven per day per cycling other road user in each country are depicted for each of the participating countries. The horizontal axis gives the percentage of cyclists in the other road users group. On average, only about 34 percent of all European other road users cycle. The vertical axis gives the average number of kilometres driven per cyclist for each of the countries. On average, a European cycling other road user cycles about seven kilometres per day. As expected countries with a tradition in cycling like the Netherlands, Germany and Finland have the highest percentage of cyclists in the other road users group. Greece, Cyprus and Spain have the lowest percentages. The percentage of cycling other road users is unrelated to the average number of kilometres driven per cyclist per day (r= 0.00). Figure 1: Percentage of cycling other road users and average number of kilometres cycled per cycling other road user per day per country.
Kilometers cycled per other road user per day Figure 2: Kilometres driven per other road user per day per country.
Kilometers cycled per other road user per day Figure 2: Kilometres driven per other road user per day per country.
Figure 3: Cycling other road users by age group.
Figure 3: Cycling other road users by age group.
Percentual share of both sexes in cycling other road users
Percentual share of both sexes in cycling other road users
To illustrate the relative independence of cycling traffic volumes and cyclists” risk perception, in Figure 6 both the cycling traffic volume per country and the perceived danger of cycling are depicted. In order to evaluate the cycling other road users perception of the risk of cycling they were asked how dangerous they considered cycling to be regarding accidents. The question had to be answered with “very”, “fairly”, “not much” or “not at all”. In order to evaluate the risk perception per country, the percentage of “very” and “fairly” answers were added for each country. The result of this calculation is shown in Figure 5. The perceived risk of cycling is the highest in Ireland, Greece and Finland. The lowest risk perception was found in Spain, Israel and Austria. All together the top and bottom three countries are neither countries with high cyclist traffic volumes, nor countries with low cycling traffic volumes.
To illustrate the relative independence of cycling traffic volumes and cyclists” risk perception, in Figure 6 both the cycling traffic volume per country and the perceived danger of cycling are depicted. In order to evaluate the cycling other road users perception of the risk of cycling they were asked how dangerous they considered cycling to be regarding accidents. The question had to be answered with “very”, “fairly”, “not much” or “not at all”. In order to evaluate the risk perception per country, the percentage of “very” and “fairly” answers were added for each country. The result of this calculation is shown in Figure 5. The perceived risk of cycling is the highest in Ireland, Greece and Finland. The lowest risk perception was found in Spain, Israel and Austria. All together the top and bottom three countries are neither countries with high cyclist traffic volumes, nor countries with low cycling traffic volumes.
Percentage that sometimes, often, very often or always cross when red is light Figure 7: Percentage of cyclists that sometimes, often, very often or always cross when the light is red.
Percentage that sometimes, often, very often or always cross when red is light Figure 7: Percentage of cyclists that sometimes, often, very often or always cross when the light is red.
Figure 8: Percentage of cyclists that often, very often or always wear reflective clothing.
Figure 8: Percentage of cyclists that often, very often or always wear reflective clothing.
Percentage of cyclists that often, very often or always wear a bicycle helmet Figure 9: Percentage of cyclists that often, very often or always wear a bicycle helmet.
Percentage of cyclists that often, very often or always wear a bicycle helmet Figure 9: Percentage of cyclists that often, very often or always wear a bicycle helmet.
Percentage of cyclists that are very or fairly satisfied with safety as a cyclist Figure 10: Percentage of cyclists that are very or fairly satisfied with safety as a cyclist.
Percentage of cyclists that are very or fairly satisfied with safety as a cyclist Figure 10: Percentage of cyclists that are very or fairly satisfied with safety as a cyclist.
Percentage of cyclists that are very or fairly satisfied with cycle paths on their usual route Figure 13: Percentage of cyclists that are very or fairly satisfied with cycle paths (Greece= 0%).
Percentage of cyclists that are very or fairly satisfied with cycle paths on their usual route Figure 13: Percentage of cyclists that are very or fairly satisfied with cycle paths (Greece= 0%).
As expected, cyclists in countries with high cyclist traffic volumes tend to be more satisfied witt the cycle paths on the route they usually take. But once again there are notable exceptions to this trend Despite the high cycling traffic volumes in Hungary, Hungarian cyclists are the second least satisfiec with cycle paths. Whereas Israeli cyclists’ satisfaction is slightly above the EU average, while being the second to last country in terms of cycling traffic volumes. Figure 14 below gives the correlatior between both variables: Figure 14: Satisfaction with cycle paths as a function of cycling traffic volumes.
As expected, cyclists in countries with high cyclist traffic volumes tend to be more satisfied witt the cycle paths on the route they usually take. But once again there are notable exceptions to this trend Despite the high cycling traffic volumes in Hungary, Hungarian cyclists are the second least satisfiec with cycle paths. Whereas Israeli cyclists’ satisfaction is slightly above the EU average, while being the second to last country in terms of cycling traffic volumes. Figure 14 below gives the correlatior between both variables: Figure 14: Satisfaction with cycle paths as a function of cycling traffic volumes.
Figure 15: Satisfaction with safety as a function of satisfaction with cycle paths.
Figure 15: Satisfaction with safety as a function of satisfaction with cycle paths.
Percentage of cyclists that are very or fairly satisfied with the speed of traffic Figure 16: Percentage of cyclists that are very or fairly satisfied with the speed of traffic.
Percentage of cyclists that are very or fairly satisfied with the speed of traffic Figure 16: Percentage of cyclists that are very or fairly satisfied with the speed of traffic.
Percentage of cyclists that are very or fairly satisfied with the volume of traffic Figure 17: Percentage of cyclists that are very or fairly satisfied with the volume of traffic.
Percentage of cyclists that are very or fairly satisfied with the volume of traffic Figure 17: Percentage of cyclists that are very or fairly satisfied with the volume of traffic.
Figure 18: Percentage of cyclists that are very or fairly satisfied with the number of street lights. Finally, the satisfaction scores for the number of street lights show less international variation then the satisfaction scores for the speed and volume of traffic. As shown in Figure 18 there is hardly any international variation in these scores, although on the low satisfaction end of the continuum Greece, Serbia and Cyprus stand out with only 0 to 40 percent satisfied cyclists, whereas on the other side of the scale the Netherlands stand out with more than 80 percent satisfied cyclists. Cyclists’ interactions with other road users
Figure 18: Percentage of cyclists that are very or fairly satisfied with the number of street lights. Finally, the satisfaction scores for the number of street lights show less international variation then the satisfaction scores for the speed and volume of traffic. As shown in Figure 18 there is hardly any international variation in these scores, although on the low satisfaction end of the continuum Greece, Serbia and Cyprus stand out with only 0 to 40 percent satisfied cyclists, whereas on the other side of the scale the Netherlands stand out with more than 80 percent satisfied cyclists. Cyclists’ interactions with other road users
Annoyance (often, very often or always) by three types of road users Figure 19: Percentage of cyclists that are often, very often or always annoyed by other road users.
Annoyance (often, very often or always) by three types of road users Figure 19: Percentage of cyclists that are often, very often or always annoyed by other road users.
Percentage of cyclists that agree that you can drink and cycle if you do it carfully Figure 20: Percentage of cyclists that agree you can drink and cycle if you do it carefully. On average, one out of four cyclists agrees with this proposition, but Figure 20 reveals huge international variations in this number. In the Netherlands, Sweden and Estonia, for instance about one out of two cyclists or more agree with this proposition, whereas in six countries less than one out of ten cyclist agree with this proposition (Greece, Cyprus, Slovenia, Spain and Hungary). Apart from Hungary the latter are all countries with relatively low cyclist traffic volumes. Possibly the zero alcohol limit in countries like Hungary and Slovenia might be related to these low percentages.
Percentage of cyclists that agree that you can drink and cycle if you do it carfully Figure 20: Percentage of cyclists that agree you can drink and cycle if you do it carefully. On average, one out of four cyclists agrees with this proposition, but Figure 20 reveals huge international variations in this number. In the Netherlands, Sweden and Estonia, for instance about one out of two cyclists or more agree with this proposition, whereas in six countries less than one out of ten cyclist agree with this proposition (Greece, Cyprus, Slovenia, Spain and Hungary). Apart from Hungary the latter are all countries with relatively low cyclist traffic volumes. Possibly the zero alcohol limit in countries like Hungary and Slovenia might be related to these low percentages.
Percentage of cyclists who agree that drink cycling substantially increases accident risk Figure 21: Percentage of cyclists that agree that drink cycling substantially increases accident.
Percentage of cyclists who agree that drink cycling substantially increases accident risk Figure 21: Percentage of cyclists that agree that drink cycling substantially increases accident.
Figure 22: Cyclists that agree that drink cycling substantially increases accident risk as a function of agreement with the idea that you can drink and cycle if you do it carefully. Somewhat surprisingly, across all countries, we did not find any correlation between both variables (cf. Figure 22). When we calculated the correlation between both variables at an individual level, we obtained a weak but significant correlation of - .19. All together these results seem to indicate that both attitudes reflect largely independent dimensions. Accident involvement as a cyclist In question ORU11 the respondents were asked to indicate whether or not they have been involved in an accident in the last three years as a pedestrian, cyclist, car passenger, motorcycle passenger or moped rider?'. In the figure below we depict the percentage of cyclists that have been involved in an accident for each country. Since accident involvement is generally hugely dependent on exposure (e.g. Elvik & Vaa, 2009), the percentage of accident involved cyclists is depicted in the figure below as a function of the average kilometres cycled per cyclist. Not cycling other road users were not taken into account in this analysis.
Figure 22: Cyclists that agree that drink cycling substantially increases accident risk as a function of agreement with the idea that you can drink and cycle if you do it carefully. Somewhat surprisingly, across all countries, we did not find any correlation between both variables (cf. Figure 22). When we calculated the correlation between both variables at an individual level, we obtained a weak but significant correlation of - .19. All together these results seem to indicate that both attitudes reflect largely independent dimensions. Accident involvement as a cyclist In question ORU11 the respondents were asked to indicate whether or not they have been involved in an accident in the last three years as a pedestrian, cyclist, car passenger, motorcycle passenger or moped rider?'. In the figure below we depict the percentage of cyclists that have been involved in an accident for each country. Since accident involvement is generally hugely dependent on exposure (e.g. Elvik & Vaa, 2009), the percentage of accident involved cyclists is depicted in the figure below as a function of the average kilometres cycled per cyclist. Not cycling other road users were not taken into account in this analysis.
Accident involvement by average kms/ day cycling Figure 23: Accident involvement by average kilometres cycled per day.
Accident involvement by average kms/ day cycling Figure 23: Accident involvement by average kilometres cycled per day.
Figure 24: Cyclist accident risk as a function of cycling traffic volumes per country.
Figure 24: Cyclist accident risk as a function of cycling traffic volumes per country.
Figure 1: Registered PTW (mopeds and motorcycles) compared to registered cars in 2008, by country.
Figure 1: Registered PTW (mopeds and motorcycles) compared to registered cars in 2008, by country.
Figure 2: Relative proportion of killed road users in 2008, by country.
Figure 2: Relative proportion of killed road users in 2008, by country.
Table 1: Comparison of killed motorcyclists and registered motorcycles relative proportions (compared with cars) in 2008, by country. Comparing road users imply to identify groups with different compositions. Indeed, comparing motorcyclists’ attitudes with car drivers’ may be biased due to other differences in sample characteristics. For example, as shown below, motorcyclists are far more male, younger, have fewer children, and are more single than car drivers. Other Road Users are older, more female and more urban than car drivers. Those specificities of each group are important to keep in mind when interpreting comparison results.
Table 1: Comparison of killed motorcyclists and registered motorcycles relative proportions (compared with cars) in 2008, by country. Comparing road users imply to identify groups with different compositions. Indeed, comparing motorcyclists’ attitudes with car drivers’ may be biased due to other differences in sample characteristics. For example, as shown below, motorcyclists are far more male, younger, have fewer children, and are more single than car drivers. Other Road Users are older, more female and more urban than car drivers. Those specificities of each group are important to keep in mind when interpreting comparison results.
Figure 3: Age distribution in each road users group.
Figure 3: Age distribution in each road users group.
The proportion of car drivers and motorcyclists that travelled as motorcycle passenger is quite similar, both groups used that way of travelling sporadically (98%, 93%). And the same happens in the category “car passenger”, though car drivers use a little bit more this mode (7%, 5% nearly every day; and 28%, 23%, uses it 1-4 times a week respectively). Also similar proportions of car and motorcycle drivers used nearly every day Public Transport (7%, 5%) and mopeds (1%, 3%).
The proportion of car drivers and motorcyclists that travelled as motorcycle passenger is quite similar, both groups used that way of travelling sporadically (98%, 93%). And the same happens in the category “car passenger”, though car drivers use a little bit more this mode (7%, 5% nearly every day; and 28%, 23%, uses it 1-4 times a week respectively). Also similar proportions of car and motorcycle drivers used nearly every day Public Transport (7%, 5%) and mopeds (1%, 3%).
Figure 1: Concern about social problems (very + fairly %) (co.02). Perceived Road Safety and Government Concern for it
Figure 1: Concern about social problems (very + fairly %) (co.02). Perceived Road Safety and Government Concern for it
Figure 2: Opinions on roads safety (very + fairly safe %) (co. 03).
Figure 2: Opinions on roads safety (very + fairly safe %) (co. 03).
Figure 3: Perceived danger of transport modes (very + fairly dangerous) (co.09).
Figure 3: Perceived danger of transport modes (very + fairly dangerous) (co.09).
Figure 4: Perceived danger of motorcycling (very + fairly dangerous %) (co.09 item 5).
Figure 4: Perceived danger of motorcycling (very + fairly dangerous %) (co.09 item 5).
Figure 5: Perceived danger of car driving (very + fairly dangerous %) (co.09 item 4).
Figure 5: Perceived danger of car driving (very + fairly dangerous %) (co.09 item 4).
Figure 6: Perception of cycling danger (very+ fairly dangerous %) (co.09 item 2). Acceptance of new in-vehicle technologies to improve safety (CO.06)
Figure 6: Perception of cycling danger (very+ fairly dangerous %) (co.09 item 2). Acceptance of new in-vehicle technologies to improve safety (CO.06)
Figure 7: In favour of speed limiters (very + fairly %) (co.06 item a).
Figure 7: In favour of speed limiters (very + fairly %) (co.06 item a).
Figure 8: In favour of «black boxes» to identify causes of accidents (very + fairly %) (co.06 item b).
Figure 8: In favour of «black boxes» to identify causes of accidents (very + fairly %) (co.06 item b).
Figure 10: In favour of “alcolock” system if driver is recidivist (very + fairly %) (co.06 item d).
Figure 10: In favour of “alcolock” system if driver is recidivist (very + fairly %) (co.06 item d).
Figure 11: In favour of fatigue detection devices (very + fairly %) (co.06 item e). However, the extent of country support also varied with the type of in-vehicle device. For example, if we compare Car Drivers’ support for Speed Limiter, Black-Box, and Alcolock (Figures 7, 8 and 9, respectively), we see shifts in the relative position of Country mean rating for the three devices. For example, only 42% of Swedish Car Drivers support in-vehicle speed limiters, but over 80% of them favour an Alcolock.
Figure 11: In favour of fatigue detection devices (very + fairly %) (co.06 item e). However, the extent of country support also varied with the type of in-vehicle device. For example, if we compare Car Drivers’ support for Speed Limiter, Black-Box, and Alcolock (Figures 7, 8 and 9, respectively), we see shifts in the relative position of Country mean rating for the three devices. For example, only 42% of Swedish Car Drivers support in-vehicle speed limiters, but over 80% of them favour an Alcolock.
Figure 12: Level of acceptance of surveillance systems (very + fairly) (co.07 items a, b and c).
Figure 12: Level of acceptance of surveillance systems (very + fairly) (co.07 items a, b and c).
Figure 13: Support for speed control measures by road user and country co.07b, co.07c and co.06a.
Figure 13: Support for speed control measures by road user and country co.07b, co.07c and co.06a.
Figure 14: In favour of possible new measures (very+ fairly %) (co.07 items d, e, f , g).
Figure 14: In favour of possible new measures (very+ fairly %) (co.07 items d, e, f , g).
Figure 15: Level of agreement with the toughening of penalties (strongly agree +agree) (co.08). In country comparisons, three countries stand out for their high levels of supporting tough sanctions: Hungary as regards tougher sanctions for speeding (91% of Other Road Users, 72% of Car Drivers and 69% of Motorcyclists) and for driving under the effects of alcohol (98% of Other Road Users); /reland in relation to sanctions for not wearing a helmet (91% Other Road Users) and for using handheld phones (89% Other Road Users); and Cyprus for not using restraint systems (89% of Car Drivers).
Figure 15: Level of agreement with the toughening of penalties (strongly agree +agree) (co.08). In country comparisons, three countries stand out for their high levels of supporting tough sanctions: Hungary as regards tougher sanctions for speeding (91% of Other Road Users, 72% of Car Drivers and 69% of Motorcyclists) and for driving under the effects of alcohol (98% of Other Road Users); /reland in relation to sanctions for not wearing a helmet (91% Other Road Users) and for using handheld phones (89% Other Road Users); and Cyprus for not using restraint systems (89% of Car Drivers).
Figure 16: Factors which may cause accidents (often + very often + always) (cd24a,b,c,e and mc26a,b,f,h).
Figure 16: Factors which may cause accidents (often + very often + always) (cd24a,b,c,e and mc26a,b,f,h).
Car drivers in our sample held a valid driver’s license on average for 20.47 years (SD= 13.93 years) and the mean mileage during the last 12 months was 15,063 kilometres (SD= 17,102 km). The majority of the car drivers in the current sample were habitual drivers. Seventy percent of them reported driving on daily basis. Thus, participants in the car driver category could be considered to be high on individual car use. Table 1: Concern for the pollution in different road user groups.
Car drivers in our sample held a valid driver’s license on average for 20.47 years (SD= 13.93 years) and the mean mileage during the last 12 months was 15,063 kilometres (SD= 17,102 km). The majority of the car drivers in the current sample were habitual drivers. Seventy percent of them reported driving on daily basis. Thus, participants in the car driver category could be considered to be high on individual car use. Table 1: Concern for the pollution in different road user groups.
Figure 1: Car drivers’ average willingness to accept changes in travel behaviour for the environment.
Figure 1: Car drivers’ average willingness to accept changes in travel behaviour for the environment.
Figure 2: Level of agreement with different reasons for motorcycle riders to choose this travel mode. eee es Oe eee — Se ee eee eee ee ee ee eee eee (1= not at all, 2= not much, 3= fairly, 4= very). Bars denote 95% confidence intervals for the mean.
Figure 2: Level of agreement with different reasons for motorcycle riders to choose this travel mode. eee es Oe eee — Se ee eee eee ee ee ee eee eee (1= not at all, 2= not much, 3= fairly, 4= very). Bars denote 95% confidence intervals for the mean.
Figure 3: Motorcycle riders’ willingness to accept changes in travel behaviour for the environment. (1= not at all, 2= not much, 3= fairly, 4= very). Bars denote 95% confidence intervals for the mean. The motorcycle riders’ (data were available for 4234 riders) willingness to accept different types of changes in their travel behaviour for the environment is shown in Figure 3. The most favourable change in travel behaviour was to have a car-free day once in a month and to increase bicycle use, though the riders were actually not very prone to these two options. They were, however, even less prone to increasing public transport use and reducing their motorcycle use. Reducing vehicle use was the least favoured change in travel behaviour.
Figure 3: Motorcycle riders’ willingness to accept changes in travel behaviour for the environment. (1= not at all, 2= not much, 3= fairly, 4= very). Bars denote 95% confidence intervals for the mean. The motorcycle riders’ (data were available for 4234 riders) willingness to accept different types of changes in their travel behaviour for the environment is shown in Figure 3. The most favourable change in travel behaviour was to have a car-free day once in a month and to increase bicycle use, though the riders were actually not very prone to these two options. They were, however, even less prone to increasing public transport use and reducing their motorcycle use. Reducing vehicle use was the least favoured change in travel behaviour.
Figure 4: Reasons for other road users to choose this travel mode.
Figure 4: Reasons for other road users to choose this travel mode.
Next, we compared car drivers and motorcycle riders on four common items measuring their willingness to accept changes in travel behaviour (see Figure 5). Motorcycle riders were less willing to reduce the use of their vehicle than car drivers, z= 28.08, p < .001, partial n’= .05. They were also less willing to increase the use of public transport, z= 13.81, p < .001, partial n’= .01, and to have a car free day, z= 4.14, p< .001, partial n°= .001. The two groups did not differ much on their willingness to change behaviour in terms of increasing bicycle use, z= 0.28, p= .782, partial n’= .00. All in all, compared to car drivers, motorcycle riders seem to be less concerned with pollution and less open to reduce vehicle use in order to prevent the effects of transport on the environment.
Next, we compared car drivers and motorcycle riders on four common items measuring their willingness to accept changes in travel behaviour (see Figure 5). Motorcycle riders were less willing to reduce the use of their vehicle than car drivers, z= 28.08, p < .001, partial n’= .05. They were also less willing to increase the use of public transport, z= 13.81, p < .001, partial n’= .01, and to have a car free day, z= 4.14, p< .001, partial n°= .001. The two groups did not differ much on their willingness to change behaviour in terms of increasing bicycle use, z= 0.28, p= .782, partial n’= .00. All in all, compared to car drivers, motorcycle riders seem to be less concerned with pollution and less open to reduce vehicle use in order to prevent the effects of transport on the environment.
Figure 6: Average motor vehicle users’ (car drivers’ and motorcycle riders’) concern about the air pollution and willingness to change travel behaviour in different countries.
Figure 6: Average motor vehicle users’ (car drivers’ and motorcycle riders’) concern about the air pollution and willingness to change travel behaviour in different countries.
Table 2: Difference in willingness to change different travel behaviours between the low-concern and high-concern countries.
Table 2: Difference in willingness to change different travel behaviours between the low-concern and high-concern countries.
Figure 7: Comparison of road user groups in terms of their concern for the air pollution across countries.
Figure 7: Comparison of road user groups in terms of their concern for the air pollution across countries.
Figure 8: Car drivers’ (CD) and motorcycle riders’ (MC) willingness to change travel behaviour for the environment in the low- and high-concern countries. Figure 8 shows the comparison between car drivers and motorcycle users coming from countries with low and high concern about the air pollution. Car drivers from high- and low-concern countries differed statistically significantly on all the studied variables: on their willingness to reduce car use, z= 9.57, p< .001, n?= .008, to increase public transport, z= 11.21, p< .001, n= .011, to have a car free day, z= 6.59, p< .001, 1’= .004, and to increase bicycle use, z= 28.52, p< .001, 1’°= .065. Motorcycle riders from high- and low-concern countries differed according to their willingness to reduce motorcycle use, z= 5.71, p< .001, n= .008, willingness to increase public transport use, z= 8.20, p< .001, 1?= .016, and willingness to increase bicycle use, z= 15.50, p < .001, n*= .054, but they did not differ according to their willingness to have a car free day, z= 1.76, p= .079, 1’= .001. In general it could be said that motor vehicle users from the high-concern countries were less willing to change their travel behaviours than the ones from the low-concern countries.
Figure 8: Car drivers’ (CD) and motorcycle riders’ (MC) willingness to change travel behaviour for the environment in the low- and high-concern countries. Figure 8 shows the comparison between car drivers and motorcycle users coming from countries with low and high concern about the air pollution. Car drivers from high- and low-concern countries differed statistically significantly on all the studied variables: on their willingness to reduce car use, z= 9.57, p< .001, n?= .008, to increase public transport, z= 11.21, p< .001, n= .011, to have a car free day, z= 6.59, p< .001, 1’= .004, and to increase bicycle use, z= 28.52, p< .001, 1’°= .065. Motorcycle riders from high- and low-concern countries differed according to their willingness to reduce motorcycle use, z= 5.71, p< .001, n= .008, willingness to increase public transport use, z= 8.20, p< .001, 1?= .016, and willingness to increase bicycle use, z= 15.50, p < .001, n*= .054, but they did not differ according to their willingness to have a car free day, z= 1.76, p= .079, 1’= .001. In general it could be said that motor vehicle users from the high-concern countries were less willing to change their travel behaviours than the ones from the low-concern countries.
Note: Green shading is used for percentages above 75. Bold indicates p < .0017’ for difference between MC and CD. ***: p<.001, *: p<.05, ns: not significant. On average, one in ten Europeans admits to driving having consumed alcohol at least once in the previous week. This rate has decreased since the previous survey (SARTRE 3: 20%) but the ranking of countries remains unchanged: the declared drink-drive behaviour is more frequent in southern countries (Italy, Cyprus, and Spain) than in eastern and northern countries (Poland, Sweden, Hungary, Czech Republic, Estonia and Finland).
Note: Green shading is used for percentages above 75. Bold indicates p < .0017’ for difference between MC and CD. ***: p<.001, *: p<.05, ns: not significant. On average, one in ten Europeans admits to driving having consumed alcohol at least once in the previous week. This rate has decreased since the previous survey (SARTRE 3: 20%) but the ranking of countries remains unchanged: the declared drink-drive behaviour is more frequent in southern countries (Italy, Cyprus, and Spain) than in eastern and northern countries (Poland, Sweden, Hungary, Czech Republic, Estonia and Finland).
Note: ***: p<.001. Reminder: higher score indicate positive answer and scales ranges are standardized. Table 2: Drink Drive Behaviours of Friends, mean scores. Regionally, we find the same trends but these are more common: there is now a significant difference (p<.001) between motorcyclists and drivers for 13 countries (Austria, Finland, France, Germany, Netherlands, Belgium, Estonia, Poland, Serbia, Spain and Sweden). Both Italy and Cyprus demonstrated the opposite result: respondents more likely to report their friends driving after drinking among motorcyclists than car drivers. Note that both those countries are characterized by their high levels of alcohol consumption in comparison to other European nations (World Health Organization, 2011).
Note: ***: p<.001. Reminder: higher score indicate positive answer and scales ranges are standardized. Table 2: Drink Drive Behaviours of Friends, mean scores. Regionally, we find the same trends but these are more common: there is now a significant difference (p<.001) between motorcyclists and drivers for 13 countries (Austria, Finland, France, Germany, Netherlands, Belgium, Estonia, Poland, Serbia, Spain and Sweden). Both Italy and Cyprus demonstrated the opposite result: respondents more likely to report their friends driving after drinking among motorcyclists than car drivers. Note that both those countries are characterized by their high levels of alcohol consumption in comparison to other European nations (World Health Organization, 2011).
the tactical level (Michon, 1979), the one which concerns the positioning among others. The difference between motorcyclists and car drivers may be accounted for by both the user's vulnerability and the vehicle handling difficulty (a car is easier to drive, see for example Lin & Kraus, 2009). Finally, we observe that if an overwhelming majority of European motorcyclists and car drivers agree on the idea that alcohol plays a major part in road traffic accidents, the most vulnerable of those two (motorcyclists) underestimate it compared to car drivers. This means that motorcyclists downplay the impact of alcohol on road accidents compared to other factors.
the tactical level (Michon, 1979), the one which concerns the positioning among others. The difference between motorcyclists and car drivers may be accounted for by both the user's vulnerability and the vehicle handling difficulty (a car is easier to drive, see for example Lin & Kraus, 2009). Finally, we observe that if an overwhelming majority of European motorcyclists and car drivers agree on the idea that alcohol plays a major part in road traffic accidents, the most vulnerable of those two (motorcyclists) underestimate it compared to car drivers. This means that motorcyclists downplay the impact of alcohol on road accidents compared to other factors.
Note: ns: not significant. Reminder: higher score indicate positive answer and scales ranges are standardized. National averages reveal a sub-group of countries where respondents are less concerned by legal risk. It mixes together countries characterized by very different drink-drive rates and by different road side tests rates, such as Italy and Sweden (in 2008, 23 vs. 287 roadside alcohol breath tests per 1000 inhabitants; European Transport Safety Council, 2008). Therefore, the relationship between perceived physical risk (crash) and perceived legal risk (punishment) seems not to be simply linear. It is possible that in a country with a high level of testing like Sweden, people underestimate the number of road- side controls because they are rarely actually driving under influence and may thus feel less concerned about road side tests. In contrast, Italians are driving under influence more frequently and may thus overestimate the probability of alcohol check even though actual control rates are very low.
Note: ns: not significant. Reminder: higher score indicate positive answer and scales ranges are standardized. National averages reveal a sub-group of countries where respondents are less concerned by legal risk. It mixes together countries characterized by very different drink-drive rates and by different road side tests rates, such as Italy and Sweden (in 2008, 23 vs. 287 roadside alcohol breath tests per 1000 inhabitants; European Transport Safety Council, 2008). Therefore, the relationship between perceived physical risk (crash) and perceived legal risk (punishment) seems not to be simply linear. It is possible that in a country with a high level of testing like Sweden, people underestimate the number of road- side controls because they are rarely actually driving under influence and may thus feel less concerned about road side tests. In contrast, Italians are driving under influence more frequently and may thus overestimate the probability of alcohol check even though actual control rates are very low.
On average, Europeans are supportive of the alcohol interlock (80%). The most favourable are ORU, followed by car drivers and then motorcyclists. The difference between groups is significant (F= 185.9, p <.001) and increases when examined in detail: the motorcyclists are more likely to reject openly the system and conversely, the least likely to fully support it (Table 5). The ORU are the most supportive of alcohol interlocks, which is not surprising considering their greater vulnerability and their lower frequency of car use. Motorcyclists, on the other side, may have a culture of less regulation or “free driving”.
On average, Europeans are supportive of the alcohol interlock (80%). The most favourable are ORU, followed by car drivers and then motorcyclists. The difference between groups is significant (F= 185.9, p <.001) and increases when examined in detail: the motorcyclists are more likely to reject openly the system and conversely, the least likely to fully support it (Table 5). The ORU are the most supportive of alcohol interlocks, which is not surprising considering their greater vulnerability and their lower frequency of car use. Motorcyclists, on the other side, may have a culture of less regulation or “free driving”.
Table 6: “Do you think that car/ motorcycle drivers should be allowed to drink ...”, Percentages. What are the drink-drive predictors?
Table 6: “Do you think that car/ motorcycle drivers should be allowed to drink ...”, Percentages. What are the drink-drive predictors?
Figure 1. “How much would you be in favour of using?”. Very + Fairly.
Figure 1. “How much would you be in favour of using?”. Very + Fairly.
Note: Gender is coded as a dummy variable with 1: male and 2: female. Accuracy of legal units estimation is coded as: 1: underestimation, 2: correct estimation, 3: overestimation. ***: p<.001, **: p<.01, ns: not significant.
Note: Gender is coded as a dummy variable with 1: male and 2: female. Accuracy of legal units estimation is coded as: 1: underestimation, 2: correct estimation, 3: overestimation. ***: p<.001, **: p<.01, ns: not significant.
Figure 2: Effect of level of legal BAC on driving under influence of a small amount of alcohol. We observe a gap between countries with the two units limit and the others (Fig.2), but there is no difference between countries with a one unit limit and countries with a zero unit limit (F, \49)= 492.61, p<.001, 1?= .06). Car drivers and motorcyclists adapt almost the same way to local legal limit (F = 60.21, p<.001, n?<.01). (1. 16923)
Figure 2: Effect of level of legal BAC on driving under influence of a small amount of alcohol. We observe a gap between countries with the two units limit and the others (Fig.2), but there is no difference between countries with a one unit limit and countries with a zero unit limit (F, \49)= 492.61, p<.001, 1?= .06). Car drivers and motorcyclists adapt almost the same way to local legal limit (F = 60.21, p<.001, n?<.01). (1. 16923)
The scarcity of medicine-affected driving is the clearest result: the number of respondents fined for drug use at the wheel in the previous year is almost zero. The difference between motorcyclists and car drivers is not significant, which could be due to the small difference in consumption, but is more likely due to the near absence of tests for drugs. Awareness of medicine-affected driving risks
The scarcity of medicine-affected driving is the clearest result: the number of respondents fined for drug use at the wheel in the previous year is almost zero. The difference between motorcyclists and car drivers is not significant, which could be due to the small difference in consumption, but is more likely due to the near absence of tests for drugs. Awareness of medicine-affected driving risks
Drugs checks are virtually nonexistent in Europe. Despite this, European drivers seem to be highly concerned by physical risks associated with medicine consumption and driving, especially if they are motorcyclists: on average 84% of car drivers and 85% of motorcyclists said they give credit to warning notices on medication packages.
Drugs checks are virtually nonexistent in Europe. Despite this, European drivers seem to be highly concerned by physical risks associated with medicine consumption and driving, especially if they are motorcyclists: on average 84% of car drivers and 85% of motorcyclists said they give credit to warning notices on medication packages.
Both car drivers and motorcyclists agree that the other drivers/motorcyclists most frequently exceed the speed limit on motorways. Slightly less, but still very frequent speeding by others is reported for main roads outside built-up areas, respectively other roads outside built-up areas. The occurrence of speeding is thought to be most rare on roads inside built-up areas.
Both car drivers and motorcyclists agree that the other drivers/motorcyclists most frequently exceed the speed limit on motorways. Slightly less, but still very frequent speeding by others is reported for main roads outside built-up areas, respectively other roads outside built-up areas. The occurrence of speeding is thought to be most rare on roads inside built-up areas.
Table 2b: How often other drivers/motorcyclists break speed limits on different road types (respondents within AVO-groups)?
Table 2b: How often other drivers/motorcyclists break speed limits on different road types (respondents within AVO-groups)?
For each road type, the percentage of respondents who had indicated that they believed that other drivers/motorcyclists often, very often or always (i.e. the AVO-group) speed, is higher for car drivers than for motorcyclists. Accordingly, the motorcyclists consider the other motorcyclists more rule- obedient.
For each road type, the percentage of respondents who had indicated that they believed that other drivers/motorcyclists often, very often or always (i.e. the AVO-group) speed, is higher for car drivers than for motorcyclists. Accordingly, the motorcyclists consider the other motorcyclists more rule- obedient.
Table 3: How often other car drivers/motorcyclists break speed limits on different road types by age? Table 4 shows the percentage of male or female respondents who think that other drivers motorcyclists would often, very often, or always speed on the different types of roads.
Table 3: How often other car drivers/motorcyclists break speed limits on different road types by age? Table 4 shows the percentage of male or female respondents who think that other drivers motorcyclists would often, very often, or always speed on the different types of roads.
Table 4: How often other car drivers/motorcyclists break speed limits on different road types by gender? With regard to car drivers’ the perception of male and female drivers was very similar when considering built-up areas. However a significant (but slight) relationship is present, since females are much more inclined to believe that others exceed the limit on motorways, main roads and country roads (y7= 13.386; df= 1; p= 0.00; eta*= 0.0011; y’= 6.821; df= 1; p= 0.009; eta?= 0.00005; y7= 9.331; df= 1; p= 0.002; eta*= 0.0007).
Table 4: How often other car drivers/motorcyclists break speed limits on different road types by gender? With regard to car drivers’ the perception of male and female drivers was very similar when considering built-up areas. However a significant (but slight) relationship is present, since females are much more inclined to believe that others exceed the limit on motorways, main roads and country roads (y7= 13.386; df= 1; p= 0.00; eta*= 0.0011; y’= 6.821; df= 1; p= 0.009; eta?= 0.00005; y7= 9.331; df= 1; p= 0.002; eta*= 0.0007).
Table 5: How often other car drivers break speed limits on different road types by annual mileage? Annual mileage in general does not make a noticeable difference in how respondents judge other drivers’ frequency of speeding (see Table 5). The exception to this general result is the finding that drivers with 5 — 10000 vehicle-kilometre records believe significantly less that others exceed the speed limit on roads in built-up areas (°,,= 13.655, p= 0.008), whereas drivers of 20000+ are more likely to believe that others speed in built-up areas. Since the annual mileage of the car drivers and motorcyclists varies a great deal amongst the respondents, an additional group has been developed for this group: this is the group of drivers with an annual mileage over 20.000 km, which includes 20% of the car drivers. 7% of the car drivers and 18% of motorcyclists do not reach an annual mileage of 1 000 kilometres. 19% of the car drivers and 40 % of the motorcyclists drive 1001-5000 kilometres/year (Tables 6).
Table 5: How often other car drivers break speed limits on different road types by annual mileage? Annual mileage in general does not make a noticeable difference in how respondents judge other drivers’ frequency of speeding (see Table 5). The exception to this general result is the finding that drivers with 5 — 10000 vehicle-kilometre records believe significantly less that others exceed the speed limit on roads in built-up areas (°,,= 13.655, p= 0.008), whereas drivers of 20000+ are more likely to believe that others speed in built-up areas. Since the annual mileage of the car drivers and motorcyclists varies a great deal amongst the respondents, an additional group has been developed for this group: this is the group of drivers with an annual mileage over 20.000 km, which includes 20% of the car drivers. 7% of the car drivers and 18% of motorcyclists do not reach an annual mileage of 1 000 kilometres. 19% of the car drivers and 40 % of the motorcyclists drive 1001-5000 kilometres/year (Tables 6).
Table 6: How often other motorcyclists break speed limits on different road types by annual mileage?
Table 6: How often other motorcyclists break speed limits on different road types by annual mileage?
Table 9: Countries with three most frequent perceptions among drivers and motorcyclists (AVO-groups of speeding by other drivers/motorcyclists. The countries have been ranked on the basis of the AVO-group’s road category ratios and those countries are included in Table 9 which have been mentioned in the first three places within that ranking, while the countries figuring in the last three places can be found in Table 10.
Table 9: Countries with three most frequent perceptions among drivers and motorcyclists (AVO-groups of speeding by other drivers/motorcyclists. The countries have been ranked on the basis of the AVO-group’s road category ratios and those countries are included in Table 9 which have been mentioned in the first three places within that ranking, while the countries figuring in the last three places can be found in Table 10.
Table 10: Countries with three least frequent perceptions among drivers and motorcyclists (AVO- groups) of speeding by others drivers/motorcyclists. On the basis of all responses given to the question on speeding, it can be said that considering all road categories the order of the respondent countries is the following: Greece, Poland and Cyprus; in contrast the responses submitted by Ireland, Finland, Austria, France and Italy indicate that the respondents are less likely to believe that others would exceed the speed limit.
Table 10: Countries with three least frequent perceptions among drivers and motorcyclists (AVO- groups) of speeding by others drivers/motorcyclists. On the basis of all responses given to the question on speeding, it can be said that considering all road categories the order of the respondent countries is the following: Greece, Poland and Cyprus; in contrast the responses submitted by Ireland, Finland, Austria, France and Italy indicate that the respondents are less likely to believe that others would exceed the speed limit.
Figure 1: How often other car drivers and motorcyclists break the speed limit in built-up areas by countries (AVO-group)?
Figure 1: How often other car drivers and motorcyclists break the speed limit in built-up areas by countries (AVO-group)?
Table 11: How often car drivers/motorcyclists will be checked for speeding? The proportions of car drivers and motorcyclists who do not believe that they will be checked for speeding are about the same (13%). For both groups of road users the ’rarely’ reply was mostly marked; however, the percentage giving this answer is higher within car drivers. The next frequent answer is ‘sometimes’, followed by ‘often’, ‘very often’ and ‘always’. The percentage of those choosing sometimes’, ‘very often’, ‘often’ and ‘always’ answers is higher amongst motorcyclist than amongst car drivers. Table 11 presents the relative frequency distribution of the answers given to the question ‘On a typical journey, how likely is it that you will be checked for speeding?’
Table 11: How often car drivers/motorcyclists will be checked for speeding? The proportions of car drivers and motorcyclists who do not believe that they will be checked for speeding are about the same (13%). For both groups of road users the ’rarely’ reply was mostly marked; however, the percentage giving this answer is higher within car drivers. The next frequent answer is ‘sometimes’, followed by ‘often’, ‘very often’ and ‘always’. The percentage of those choosing sometimes’, ‘very often’, ‘often’ and ‘always’ answers is higher amongst motorcyclist than amongst car drivers. Table 11 presents the relative frequency distribution of the answers given to the question ‘On a typical journey, how likely is it that you will be checked for speeding?’
When examining the age categories we observe that the AVO-group’s percentage for car drivers and motorcyclists decreases over 45 and 55 years, respectively (see Table 12). The frequency of expected speed control is significantly higher in the age group of 25-34 years old, whilst it is significantly lower for the 65+ group (CD: x°,= 35.33, p<0.,00; MC: y7,,= 22.114, p<0.00). There is a slight, but significant, correlation between age and the frequency of expected speed control (CD: r= -0.09, p<0.01; MC: r= -0.07, p<0.01). Both amongst car drivers and motorcyclists, males more than females think that police always, very often, or often, control speed.
When examining the age categories we observe that the AVO-group’s percentage for car drivers and motorcyclists decreases over 45 and 55 years, respectively (see Table 12). The frequency of expected speed control is significantly higher in the age group of 25-34 years old, whilst it is significantly lower for the 65+ group (CD: x°,= 35.33, p<0.,00; MC: y7,,= 22.114, p<0.00). There is a slight, but significant, correlation between age and the frequency of expected speed control (CD: r= -0.09, p<0.01; MC: r= -0.07, p<0.01). Both amongst car drivers and motorcyclists, males more than females think that police always, very often, or often, control speed.
There is a significant relationship between the estimation of the frequency of speed control and the annual driving mileage (CD: r= 0.08, p<0.01; MC: r= 0.05, p<0.01), see Table 13. As the number of kilometres driven increases, the AVO-group’s percentage increases as well. The expected frequency of speed control is more significantly lower than the average for motor vehicle drivers with < 10 000 km, and for motorcyclists with only < 1000 km annual mileage. The expected frequency of speed control is more significantly higher for car drivers with 20 000+ km, and for motorcyclists with 10 000+ km annual mileage. Table 13: How often car drivers/motorcyclists will be checked for speeding by annual mileage?
There is a significant relationship between the estimation of the frequency of speed control and the annual driving mileage (CD: r= 0.08, p<0.01; MC: r= 0.05, p<0.01), see Table 13. As the number of kilometres driven increases, the AVO-group’s percentage increases as well. The expected frequency of speed control is more significantly lower than the average for motor vehicle drivers with < 10 000 km, and for motorcyclists with only < 1000 km annual mileage. The expected frequency of speed control is more significantly higher for car drivers with 20 000+ km, and for motorcyclists with 10 000+ km annual mileage. Table 13: How often car drivers/motorcyclists will be checked for speeding by annual mileage?
Table 14: How often car drivers/motorcyclists will be checked for speeding by engine size? If the car driver’s answers are examined in relation to the engine size of the vehicle driven, one can see that for car drivers a significant higher AVO-group’s percentage goes together with larger engine size. For motorcyclists, except the users of motorcycle with 125-250 cc engine size, it may be stated, that the frequency of expected speed control does not differs more significantly from the average (approximately 20%), in the case of the above mentioned engine size category it is lower by 5% s- 9.73, p<0.045)._
Table 14: How often car drivers/motorcyclists will be checked for speeding by engine size? If the car driver’s answers are examined in relation to the engine size of the vehicle driven, one can see that for car drivers a significant higher AVO-group’s percentage goes together with larger engine size. For motorcyclists, except the users of motorcycle with 125-250 cc engine size, it may be stated, that the frequency of expected speed control does not differs more significantly from the average (approximately 20%), in the case of the above mentioned engine size category it is lower by 5% s- 9.73, p<0.045)._
Figure 2: How often car drivers and motorcyclists will be checked for speeding by countries (AVO-group)?
Figure 2: How often car drivers and motorcyclists will be checked for speeding by countries (AVO-group)?
Table 15: Car drivers and motorcyclists’ experience of punishment for breaking the speed limit in the past 3 years by age groups. Ifthe age categories are examined, one can state that for car drivers and motorcyclists, the percentage of those punished in the last three years is highest in the age group of 25-34 years and lowest among those over 65 (see Table 15). In the case of motorcyclists those over 45 and in that of car drivers the ones over 55 the rate of the punished for speeding is significantly below the average value (CD: a 57.170, p<0.000, MC: 4?,.= 37.21, p<0.000).
Table 15: Car drivers and motorcyclists’ experience of punishment for breaking the speed limit in the past 3 years by age groups. Ifthe age categories are examined, one can state that for car drivers and motorcyclists, the percentage of those punished in the last three years is highest in the age group of 25-34 years and lowest among those over 65 (see Table 15). In the case of motorcyclists those over 45 and in that of car drivers the ones over 55 the rate of the punished for speeding is significantly below the average value (CD: a 57.170, p<0.000, MC: 4?,.= 37.21, p<0.000).
Table 16: Car drivers and motorcyclists’ experience of punishment for breaking the speed limit in the past 3 years by gender.
Table 16: Car drivers and motorcyclists’ experience of punishment for breaking the speed limit in the past 3 years by gender.
Table 17: Car drivers and motorcyclists experience of punishment for breaking the speed limit in the past 3 years by annual mileage. Car drivers who reported to drive more than 20 000 kilometres in the last 12 months are most likely to have been punished for speeding during the last 3 years (see Table 17). The percentage of speed punishment receivers among motorcyclists is the highest for riders with a record over 10 000 km annual mileage. This percentage, however, — in accordance with their lower annual mileage — is lower (28%) in comparison with the car drivers’ group. Motorcyclists with more than 5 000 km annual mileage and the car drivers with a yearly record of over 10 000 km were more frequently punished for speeding than the average (CD: ¥. ay 424.784, p<0.000, eta*= 0.034; MC: x”,,.= 75.974, p<0.000, eta*= 0.017).
Table 17: Car drivers and motorcyclists experience of punishment for breaking the speed limit in the past 3 years by annual mileage. Car drivers who reported to drive more than 20 000 kilometres in the last 12 months are most likely to have been punished for speeding during the last 3 years (see Table 17). The percentage of speed punishment receivers among motorcyclists is the highest for riders with a record over 10 000 km annual mileage. This percentage, however, — in accordance with their lower annual mileage — is lower (28%) in comparison with the car drivers’ group. Motorcyclists with more than 5 000 km annual mileage and the car drivers with a yearly record of over 10 000 km were more frequently punished for speeding than the average (CD: ¥. ay 424.784, p<0.000, eta*= 0.034; MC: x”,,.= 75.974, p<0.000, eta*= 0.017).
Table 18: Car drivers and motorcyclists’ experience of punishment for breaking the speed limit in the past 3 years by engine size. The data shown in Table 18 indicate a relationship between the engine size of the vehicle driven and the punishment for excessive speeding. Both for car drivers and for the motorcyclists it is found that the percentage of drivers/motorcyclists who were punished for speeding is higher amongst drivers/ motorcyclists of large engine size motor vehicles. Drivers of cars with 1300+ cc engine size and the motorcyclists with 250+ cc engine size are punished more frequently for speeding than others (CD: Yam 146.392, p<0.000, eta’= 0.012; MC: x°,,= 50.98, p<0.000, eta*= 0.011). In both vehicle categories the drivers of vehicles equipped with engines with lower engine size were punished for speeding to a significantly lower rate.
Table 18: Car drivers and motorcyclists’ experience of punishment for breaking the speed limit in the past 3 years by engine size. The data shown in Table 18 indicate a relationship between the engine size of the vehicle driven and the punishment for excessive speeding. Both for car drivers and for the motorcyclists it is found that the percentage of drivers/motorcyclists who were punished for speeding is higher amongst drivers/ motorcyclists of large engine size motor vehicles. Drivers of cars with 1300+ cc engine size and the motorcyclists with 250+ cc engine size are punished more frequently for speeding than others (CD: Yam 146.392, p<0.000, eta’= 0.012; MC: x°,,= 50.98, p<0.000, eta*= 0.011). In both vehicle categories the drivers of vehicles equipped with engines with lower engine size were punished for speeding to a significantly lower rate.
Figure 3: Number of speed tickets in 2008 and experience of car drivers and motorcyclists with punishment for breaking the speed limit in the past 3 years, by countries*'. The average percentage of a given country’s punished responders for speeding in the last 3 years as well as the number of penalties levied for speeding in 2008 were shown in the macro-level correlation analysis.
Figure 3: Number of speed tickets in 2008 and experience of car drivers and motorcyclists with punishment for breaking the speed limit in the past 3 years, by countries*'. The average percentage of a given country’s punished responders for speeding in the last 3 years as well as the number of penalties levied for speeding in 2008 were shown in the macro-level correlation analysis.
Table 19: Summary of different nationalities of car drivers or motorcyclist scoring highest or lowest on perceived speed violations by others, experiences with speed controls and speed punishment.
Table 19: Summary of different nationalities of car drivers or motorcyclist scoring highest or lowest on perceived speed violations by others, experiences with speed controls and speed punishment.
* Rounded numbers ** Source: Speed Camera Database accessed 27" October 2011 http://www.scdb.info/ Sources Intensity of fixed speed camera enforcement in a number of European countries. Complementary table
* Rounded numbers ** Source: Speed Camera Database accessed 27" October 2011 http://www.scdb.info/ Sources Intensity of fixed speed camera enforcement in a number of European countries. Complementary table
D. Region (Please see the Eurostat NUTS document. Take the NUTS1 level definition. For exemple a questionnaire filled in Paris refers to «France», NUTS1 «lle de France», code FR1, i will be coded here as 01, 2 digits )
D. Region (Please see the Eurostat NUTS document. Take the NUTS1 level definition. For exemple a questionnaire filled in Paris refers to «France», NUTS1 «lle de France», code FR1, i will be coded here as 01, 2 digits )
COO09. How dangerous do you consider each of the following transportation modes to be regarding accidents?
COO09. How dangerous do you consider each of the following transportation modes to be regarding accidents?
CDO9. I’m going to read some statements to you concerning drinking and driving a car. Please tell me in each case how much you agree.
CDO9. I’m going to read some statements to you concerning drinking and driving a car. Please tell me in each case how much you agree.
MCO9. In the past 3 years, have you been fined, or punished in any other way, for not wearing/fastening a helmet? MCO08. I’m going to read out some statements to you concerning helmets. Please tell me in each case how much you agree.
MCO9. In the past 3 years, have you been fined, or punished in any other way, for not wearing/fastening a helmet? MCO08. I’m going to read out some statements to you concerning helmets. Please tell me in each case how much you agree.
4.) Touring-Enduro, Enduro or Offroad style Motorcycle
4.) Touring-Enduro, Enduro or Offroad style Motorcycle
Source: Ad. 1. and 2 - Eurostat (2010) : http://epp.eurostat.ec.europa.eu/; Ad. 3. - NationMaster: http://www. nationmaster.com/graph/peo_per_liv_in_urb_are-people-percentage-living-urban-areas (SRB) * - Official report from Serbian Republic Statistics Institute, 2002
Source: Ad. 1. and 2 - Eurostat (2010) : http://epp.eurostat.ec.europa.eu/; Ad. 3. - NationMaster: http://www. nationmaster.com/graph/peo_per_liv_in_urb_are-people-percentage-living-urban-areas (SRB) * - Official report from Serbian Republic Statistics Institute, 2002
Source: (SRB)* - Serbian Traffic Safety Law (“Offical Journal’, no. 41, 2nd June 2009)
Source: (SRB)* - Serbian Traffic Safety Law (“Offical Journal’, no. 41, 2nd June 2009)
Source: DG MOVE: http://ec.europa.eu/transport/road_safety/observatory/doc/alcohol_rules.pdf ; International Center for Alcohol Policies (ICAP) http://www. icap.org/PolicyIssues/DrinkingandDriving; SARTRE 3 Contextual data Tables (SRB) * - Serbian Traffic Safety Law («Offical Journal», no. 41, 2nd June 2009)
Source: DG MOVE: http://ec.europa.eu/transport/road_safety/observatory/doc/alcohol_rules.pdf ; International Center for Alcohol Policies (ICAP) http://www. icap.org/PolicyIssues/DrinkingandDriving; SARTRE 3 Contextual data Tables (SRB) * - Serbian Traffic Safety Law («Offical Journal», no. 41, 2nd June 2009)
Source: SARTRE 3 Contextual data Tables
Source: SARTRE 3 Contextual data Tables
Source: DG MOVE: http://ec.europa.eu/transport/road_safety/observatory/doc/speed_rules.pdf
Source: DG MOVE: http://ec.europa.eu/transport/road_safety/observatory/doc/speed_rules.pdf
Source: ETSC: PIN Flash n.16 Tackling the three main killers on the Road. A priority for the forthcoming EU Road Safety Action Programme http://www.etsc.eu/documents/05.05%20-%20PIN%20Flash%2016.pdf (HU) * - The number of speed tickets in classical sense is 80458; but the number of automatically issued administrative fine for speeding detected by speed cameras is 353107, in total: 433565 in Hungary (SRB) *-Annual Serbian Traffic Police Directorate data (2006-2009)
Source: ETSC: PIN Flash n.16 Tackling the three main killers on the Road. A priority for the forthcoming EU Road Safety Action Programme http://www.etsc.eu/documents/05.05%20-%20PIN%20Flash%2016.pdf (HU) * - The number of speed tickets in classical sense is 80458; but the number of automatically issued administrative fine for speeding detected by speed cameras is 353107, in total: 433565 in Hungary (SRB) *-Annual Serbian Traffic Police Directorate data (2006-2009)
Source: SARTRE 3 Contextual data Tables
Source: SARTRE 3 Contextual data Tables
Source: ETSC: PIN Flash n.16 Tackling the three main killers on the Road. A priority for the forthcoming EU Road Safety Action Programme http://www.etsc.eu/documents/05.05%20-%20PIN%20Flash%2016.pdf
Source: ETSC: PIN Flash n.16 Tackling the three main killers on the Road. A priority for the forthcoming EU Road Safety Action Programme http://www.etsc.eu/documents/05.05%20-%20PIN%20Flash%2016.pdf
33. Daytime seat belt wearing rate on rear seats aggregated of cars < 3,5 tons from road side independent survey Source: ETSC: PIN Flash n.16 Tackling the three main killers on the Road. A priority for the forthcoming EU Road Safety Action Programme http://www.etsc.eu/documents/05.05%20-%20PIN%20Flash%2016.pdf
33. Daytime seat belt wearing rate on rear seats aggregated of cars < 3,5 tons from road side independent survey Source: ETSC: PIN Flash n.16 Tackling the three main killers on the Road. A priority for the forthcoming EU Road Safety Action Programme http://www.etsc.eu/documents/05.05%20-%20PIN%20Flash%2016.pdf
Other road traffic safety relevant aspects
Other road traffic safety relevant aspects
Source: SARTRE 3 Contextual data Tables and http://www.ecllulat-news.com/car_bans (FI) * - In 1996, the Road Traffic Law in Finland was changed so that traffic offences and violations will be interfered more efficiently, especially those done by novice drivers. The follow-up time for the monitoring of offences was changed from one to two years, and the measures become stricter and more effective after each offence. The driving ban would be given after 4 offences done in 2 years time or 3 offences done in one year. However, there are no points associated with the offences, although basically the system is quite similar than the penalty point system used in many countries.
Source: SARTRE 3 Contextual data Tables and http://www.ecllulat-news.com/car_bans (FI) * - In 1996, the Road Traffic Law in Finland was changed so that traffic offences and violations will be interfered more efficiently, especially those done by novice drivers. The follow-up time for the monitoring of offences was changed from one to two years, and the measures become stricter and more effective after each offence. The driving ban would be given after 4 offences done in 2 years time or 3 offences done in one year. However, there are no points associated with the offences, although basically the system is quite similar than the penalty point system used in many countries.
Source: Ad. 39 — http://www.ecllulat-news.com/car_bans, SARTRE 3 Contextual data Tables; Ad. 40 - SARTRE 3 Contextual data Tables; Ad. 41 - DG MOVE: http://ec.europa.eu/transport/road_safety/observatory doc/drl_rules.pdf; SARTRE 3 Contextual data Tables
Source: Ad. 39 — http://www.ecllulat-news.com/car_bans, SARTRE 3 Contextual data Tables; Ad. 40 - SARTRE 3 Contextual data Tables; Ad. 41 - DG MOVE: http://ec.europa.eu/transport/road_safety/observatory doc/drl_rules.pdf; SARTRE 3 Contextual data Tables
Source: CARE Database, 2010-OECD: A record decade for road safety. Press release, 15 September 2010. (NL) * - Dutch data are official registered traffic fatalities, there are also official higher estimates of real numbe of fatalities. (SRB) *- Annual Serbian Traffic Police Directorate data (2001-2009)
Source: CARE Database, 2010-OECD: A record decade for road safety. Press release, 15 September 2010. (NL) * - Dutch data are official registered traffic fatalities, there are also official higher estimates of real numbe of fatalities. (SRB) *- Annual Serbian Traffic Police Directorate data (2001-2009)

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