Abstract
To date, electric bikers’ (e-bikers’) red-light running (RLR) behavior is often viewed as one of the main contributors to e-bike-related accidents, especially for traffic scenarios with high e-bike ridership. In this paper, we aim to understand e-bikers’ RLR behavior based on structural equation modeling. Specifically, the predictive utility of the theory of planned behavior (TPB), prototype willingness model (PWM), and their combined form, TPB-PWM model, is used to analyze e-bikers’ RLR behavior, and a comparison analysis is made. The analyses of the three modeling approaches are based on the survey data collected from two online questionnaires, where more than 1,035 participant-completed questionnaires are received. The main findings in this paper are as follows: (i) Both PWM and TPB-PWM models could work better in characterizing e-bikers’ RLR behavior than the TPB model. The former two models explain more than 80% (81.3% and 81.4%, respectively) of the variance in e-bikers’ RLR behavior, which is remarkably higher than that of the TPB model (only 74.3%). (ii) It is also revealed that RLR willingness contributes more on influencing the RLR behavior than RLR intention, which implies that such behavior is dominated by social reactive decision-making rather than the reasoned one. (iii) Among the examined psychological factors, attitude, perceived behavioral control, past behavior, prototype perceptions (favorability and similarity), RLR intention, and RLR willingness were the crucial predictors of e-bikers’ RLR behavior. Our findings also support designing of more effective behavior-change interventions to better target e-bikers’ RLR behavior by considering the influence of the identified psychological factors.
Highlights
In recent decades, as a green, cost-effective, and easy-tocarry transport means, electric bikes (e-bikes) have been adopted and promoted in an increasing number of countries such as Switzerland, Norway, the Netherlands, and China [1,2,3,4]
red-light running (RLR) intention, RLR willingness, and RLR behavior positively and significantly correlated with attitude, perceived behavioral control, past behavior, prototype similarity, and prototype favorability, while negatively and significantly with subjective norm
RLR behavior was positively and significantly related to RLR intention and willingness. e correlation analysis results support the efficacy of theory of planned behavior (TPB), prototype willingness model (PWM), and TPB-PWM frameworks in explaining e-bikers’ RLR behavior
Summary
As a green, cost-effective, and easy-tocarry transport means, electric bikes (e-bikes) have been adopted and promoted in an increasing number of countries such as Switzerland, Norway, the Netherlands, and China [1,2,3,4]. The use of e-bikes has been causing increasing e-bike-involved traffic accidents [1, 5, 6]. In China, around 56,200 traffic accidents were caused by e-bikers, resulting in 8,431 fatalities, 63,400 injuries, and direct property losses of 111 million-yuan (equivalent to 16.42 million dollars based on the 2017 average closing exchange rate) between 2013 and 2017 [7]. According to the statistics of accidents related to two-wheeled vehicles (e.g., e-bikes, regular bicycles, and e-scooters) in typical Chinese cities (including Beijing, Changchun, Ningbo, Foshan, and Weihai), e-bike-involved accidents accounted for 34.8% of the total accidents from July 2011 to June 2016; of the e-bikeinvolved accidents, the proportions of minor injuries, serious injuries, and fatalities to e-bikers were 70.0%, 12.6%, and 10.6%, respectively [8]. Previous findings showed that traffic violating behaviors, especially red-light running (RLR) behavior at signalized intersections, partially contributed to e-bikers-related
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