关键词: 交通工程, 事故严重程度, 均值和方差异质性随机参数Logit模型, 平均边际效应, 时间不稳定性

Abstract: Electric bikes (e-bikes) are experiencing rapid development and growing popularity in China. In order to investigate the influencing factors of crash severity between e-bikes and vulnerable road users under the technical standard update, and to analyze the possible heterogeneity and temporal instability. This study aims to investigate the factors influencing the crash severity between e- bikes and vulnerable road users under the update of technical standards by analyzing potential heterogeneity and temporal instability. Using a dataset of 6022 e-bikes crashes collected in Yancheng, China, potential contributing factors to injury severity were selected from five aspects: rider, accident, road, time, and environment. To explore the potential heterogeneity, a random parameter Logit model with heterogeneity in means and variances was employed. The log-likelihood ratio was utilized to test the temporal instability of the factors influencing crash injury severities. The average marginal effects of each variable were calculated to measure their impacts on the injury severity of e-bike crashes. The results showed that the random parameter Logit model with heterogeneity in means and variances outperforms both the standard model without considering heterogeneity and the binary Logit model in terms of fitting and accuracy. Moreover, under the influence of the 2019 national standards, there is significant temporal instability in the factors affecting e-bike crash severity, resulting in substantial changes in the important influencing variables before and after the update of the national standards. In the old-standard model, the variables of e-bikes and no controls are random variables, and the factors of rural roads and sand roads increase their mean and variance respectively. In the new-standard model, the variables of e-bikes and sign markings are random variables, and the factors of autumn and hit-and-run increase the mean of the e-bikes parameter. The research findings offer valuable insights for developing interventions for road traffic crashes involving e bikes and provide a theoretical support for updating the current technical standards for e-bike safety.

Key words: traffic engineering, crash injury severity, the random parameter Logit model with heterogeneity in means and variances, marginal effect, time instability