Exploring the heterogeneous effects of riding behaviours and road conditions on delivery rider severities in scooter-style electric bicycle crashes involving vehicles

Jingfeng Ma, Qi Cao, Gang Ren*, Yuanxiang Yang, Yue Deng, Jingzhi Li

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Delivery riders are more vulnerable than other traffic participants, especially in vehicle-involved delivery crashes. This study aims at identifying the unobserved heterogeneities in different factors, based on 4251 vehicle-scooter-style electric bicycle (SSEB) crashes. First, some potential factors are selected from seven perspectives, and the spatiotemporal characteristics are analysed. Second, a latent class clustering method is proposed to clarify the optimal number of clusters by maximizing the heterogeneities across clusters. Third, partial proportional odds (PPO) models for the whole dataset and sub-datasets are developed to explore the heterogeneities across various clusters. Besides, marginal effects are implemented to quantify the heterogeneities. The results evidence that there are remarkable heterogeneities across different clusters, especially in riding behaviours and road conditions. Several factors only significantly affect particular clusters but not the whole dataset. The PPO models for the sub-datasets perform better in identifying the underlying heterogeneities. The results also highlight the greater roles of riding behaviours and road conditions in delivery SSEB-vehicle crashes. The top five influencing factors are running red light, using cell phones, vehicle type, reverse riding and bike lane (their maximum marginal effects exceeding +35%). The findings could support to mitigate the related crash losses.

Original languageEnglish
Number of pages16
JournalInternational Journal of Injury Control and Safety Promotion
DOIs
Publication statusE-pub ahead of print - 9 Nov 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • heterogeneity
  • latent class clustering
  • partial proportional odds model
  • riding behaviours
  • road conditions
  • Vehicle-SSEB crashes

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