Abstract
Accidents involving electric bicycles, a popular means of transportation in China during peak traffic periods, have increased. However, studies have seldom attempted to detect the unique crash consequences during this period. This study aims to explore the factors influencing injury severity in electric bicyclists during peak traffic periods and provide recommendations to help devise specific management strategies. The random-parameters logit or mixed logit model is used to identify the relationship between different factors and injury severity. The injury severity is divided into four categories. The analysis uses automobile and electric bicycle crash data of Xi’an, China, between 2014 and 2019. During the peak traffic periods, the impact of low visibility significantly varies with factors such as areas with traffic control or without streetlights. Furthermore, compared with traveling in a straight line, three different turnings before the crash reduce the likelihood of severe injuries. Roadside protection trees are the most crucial measure guaranteeing riders’ safety during peak traffic periods. This study reveals the direction, magnitude, and randomness of factors that contribute to electric bicycle crashes. The results can help safety authorities devise targeted transportation safety management and planning strategies for peak traffic periods.
Highlights
Peak periods have the highest probability of road accidents worldwide
The log-likelihood ratio test illustrated a test statistic of 128.77 with 32 degrees of freedom (p < 0.001), which implies that the peak traffic period must be modeled separately with a confidence interval of more than 99%
According to the model separation test, each test statistic and the corresponding degrees of freedom suggest that the peak period must be modeled separately among electric bike-involved crashes with more than 99% confidence (LR peako f f − peak = 365.2, df = 40; LRo f f − peakopeak = 493.4, df = 43)
Summary
Peak periods have the highest probability of road accidents worldwide. A high traffic flow, riders’ eagerness to reach their destination, and the pressure of congestion contribute to the likelihood of accidents during this period. The number of crashes occurring during peak hours is dramatically higher than in off-peak hours [1]. Existing studies on peak periods tend to focus on automobile driver injury severity on highways [2]. Some studies highlight other unique indicators related to peak periods, such as driver distraction [6] and traveler choice [7]. The traffic management must be trained and the relevant facilities upgraded with respect to the characteristics and influencing factors of this period rather than those of the off-peak period
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