Abstract
Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.
Highlights
Electric bicycle has become a popular travel tool due to the advantages of mobility, flexibility, and environmental protection [1]
Model comparison indicated that the random parameter logit (RPL) model is statistically superior to the multinomial logit (MNL) model. e parameter estimation results excluding the fixed-effects modeling results are shown in Table 2, including the loglikelihood, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)
With regard to the effect of collision avoidance behaviors on the injury severity outcomes, the parameters of driver decelerating, driver decelerating and lane change, and cyclist decelerating and lane change are all found to be random and statistically significant. ese variables will reduce the likelihood of fatal crashes. e parameters of driver decelerating are random and have a mean of −6.351 and standard deviation of 4.851
Summary
Electric bicycle has become a popular travel tool due to the advantages of mobility, flexibility, and environmental protection [1]. E rate of electric-bicycle-related casualties was significantly increased [2]. Despite the safety issues brought about by electric bicycles, literature review found that most of the studies about cyclist injury severity were focused on traditional bicycles. The electric bicycle safety issues have been studied in the United States, Canada, Europe, Japan, and some other countries [4, 5]. Under the background of the rapid outbreak of electric bicycles in China, the factors and degree of influence may be different from developed countries
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