交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 231-240.DOI: 10.16097/j.cnki.1009-6744.2025.01.022

• 系统工程理论与方法 • 上一篇    下一篇

技术标准更新视角下电动自行车事故严重程度影响因素演化分析

邬岚,周佳雨,李根*   

  1. 南京林业大学,汽车与交通工程学院,南京210037
  • 收稿日期:2024-11-19 修回日期:2024-12-17 接受日期:2025-01-09 出版日期:2025-02-25 发布日期:2025-02-24
  • 作者简介:邬岚(1977—),女,湖北武汉人,副教授。
  • 基金资助:
    江苏省自然科学基金(BK20240678);江苏省高校哲学社会科学项目(2024SJYB0142)。

Evolutionary Analysis of Factors Influencing Electric Bike Crash Severity from Perspective of Technical Standard Update

WU Lan, ZHOUJiayu, LI Gen*   

  1. College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
  • Received:2024-11-19 Revised:2024-12-17 Accepted:2025-01-09 Online:2025-02-25 Published:2025-02-24
  • Supported by:
    Natural Science Foundation of Jiangsu Province (BK20240678);Social Science Project of Colleges and Universities in Jiangsu Province (2024SJYB0142)。

摘要: 电动自行车在我国发展迅猛,普及率高。为探究技术标准更新对电动自行车与其他弱势道路使用者之间事故严重的影响,本文分析其异质性和时间不稳定性。以盐城市2013—2023年间6022起相关交通事故为研究对象,从骑行者特征、事故特征、道路特征、时间特征、环境特征这5个方面选取潜在特征变量,通过建立均值和方差异质性随机参数模型来探索潜在异质性。同时,利用对数似然比检验事故严重程度影响因素的时间不稳定性,并借助平均边际效应量化各因素对事故严重程度的影响变化。结果表明:相比二元Logit模型和未考虑异质性的随机参数模型,均值和方差异质性随机参数Logit模型具有更高的拟合优度和模型精度。此外,在2019年国标更新的影响下,电动自行车事故严重程度的因素存在明显的时间不稳定性,导致国标更新前后的重要影响变量发生显著变化。标准更新前的模型中,电动自行车和无控制这2个变量为随机变量,农村道路和沙土这2个因素分别增加其均值和方差;标准更新后的模型中,电动自行车和标志标线这2个变量为随机变量,秋天和肇事逃逸这2个因素增加电动自行车参数的均值。研究结果为涉及电动自行车的道路交通事故制定干预措施提供有用信息,为当前进一步更新电动自行车安全技术标准提供了有力的理论支撑。

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

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