Insights into experiences and risk perception of riders of fast e-bikes
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1 Insights into experiences and risk perception of riders of fast e-bikes Young Researchers Seminar, 17-19th of June 2015, Rome Andrea Uhr, MSc in Psychology
2 Contents 1. Background 2. Survey Theoretical Background Research questions and assumed model Method Results Discussion 3. Conclusions Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
3 1. Background
4 Definition E-bikes: Bicycles with electric assistance up to a certain speed Slow e-bikes assistance up to 25 kph engine power up to 500 W legally classified as moped Fast e-bikes assistance up to 45 kph engine power up to 1000 W legally classified as moped Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
5 Number of e-bikes bfu Council for accident prevention Number of sales in Switzerland Year All e-bikes Fast e-bikes Slow e-bikes cumulated Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
6 absolute number bfu Council for accident prevention Development of crash data year + 70% slight injury severe injury or death Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
7 Types of accidents bicycle 58% 38% 5% e-bike 46% 49% 5% 0% 20% 40% 60% 80% 100% collision single accident others Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
8 2. Survey
9 Theoretical Background Road user behaviour Important determinant for road safety (Horswill & Helman, 2003; Näätänen & Summala, 1976) Significant correlations with involvement in accidents or near misses (Iversen & Rundmo, 2004) Psychological predictors for (safety-oriented) driving behaviour Perception of risk (Ranney, 1994) Perceived behavioural control (Paris & Broucke, 2008; Wallén et al., 2008) Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
10 Research questions Which psychological factors are associated with riding behaviour among riders of fast e-bikes in Switzerland? Additionally: Is there a correlation between (self-reported) riding behaviour and accident rates? Descriptive statistics such as knowledge of legal regulations and helmet usage rate Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
11 Model for predicting (self-reported) riding behaviour bfu Council for accident prevention Control variables Socio-demographic variables Gender Age Language Riding experience Duration of e-bike use Frequency of use (dichot.) Riding experience conventional bicycle (dichot.) Knowledge of accident occurrence of e-bikes Risk perception Risk perception: e-bikes in general Risk perception: e-bike speed Perspective taking Perceived behavioural control Perceived behavioural control: general Perceived behavioural control: speed Feeling of invulnerability Riding behaviour Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
12 Method Survey, paper questionnaire Sample N = 2,158 Riders of fast e-bikes, at least 1 month s riding experience M = 50 years (SD = 11.4), Min = 14 years, Max = 87 years 50,3% men, 49,7% women Riding experience: M = 37 months (SD = 33) Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
13 Questionnaire: Model: predictors & control variables For each predictor 2-5 items e.g. risk perception: e-bikes in general: Riding an e-bike is more dangerous than riding a regular bike. 1 = strongly disagree, 4 = strongly agree Internal consistencies of scales low (Cronbach s alpha from α =.340 to α =.656) Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
14 Results Descriptive statistics (selection) Riding experience conventional bicycle (n = 2153) 61% 30% 10% 0% 20% 40% 60% 80% 100% regularly occasionally (almost) never Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
15 Other road users underestimate speed of e-bike riders (n = 2118) 1% 3% 34% 62% 0% 20% 40% 60% 80% 100% Longer braking distances in comparison to regular bikes (n = 2149 Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
16 100% 90% 80% 84% 95% Accident experience 70% 60% 50% 40% 30% 20% 13% 10% 4% 3% 1% 0% Number of single accidents / falls (n = 2158) Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
17 Model verification bfu Council for accident prevention Control variables Socio-demographic variables Gender Age Language Riding experience Duration of e-bike use Frequency of use (dichot.) Riding experience conventional bicycle (dichot.) Knowledge of accident occurrence of e-bikes Risk perception Risk perception: e-bikes in general Risk perception: e-bike speed Perspective taking R 2 =.17 Perceived behavioural control Perceived behavioural control: general Perceived behavioural control: speed Feeling of invulnerability Riding behaviour Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
18 Relationship between riding behaviour and accident rate Accident rate = Accidents per year (no data of exposure available) No significant correlation for single accidents Significant but weak correlation for collisions Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
19 Discussion Riders of fast e-bikes don t misjudge risk factor speed High awareness of underestimation of e-bike speed by other road users But: risk perception regarding speed only weak correlation with riding behaviour and in unexpected (negative) direction Best predictors for self-reported riding behaviour: age & gender Psychological predictors: risk perception e-bike speed, perceived behavioural control (general & speed), feeling of invulnerability Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
20 Generally surprising directions of associations Cognitions tend to reflect behaviour shown rather than to influence behaviour E.g. Feeling of control because of cautious riding E.g. Risk perception (speed) because of risky riding No significant relationship between riding behaviour and accident rate for single accidents Significant but weak correlation between riding behaviour and accident rate for collisions Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
21 3. Conclusions
22 Conclusions Initial insight into experiences and risk perception of riders of fast e-bikes Identification of various cognitive factors related to riding behaviour No statements on any interventions possible (cognitions tend to reflect behaviour) Limitations: Riding behaviour measured by means of self-reporting No exposure data to calculate accident rate Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
23 Future research: Validate results by objective measurements of riding behaviour Factors associated with accident experience (including cognitive variables and riding behaviour) Longitudinal studies for examination of directions of relationships and assumptions of causality Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
24 Questions? Experiences and risk percpetion of riders of fast e-bikes, Andrea Uhr 17-19th of June
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