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

• Published in: International journal of injury control and safety promotion Vol. 31; no. 2; pp. 165 - 180
• Main Authors: Ma, Jingfeng, Cao, Qi, Ren, Gang, Yang, Yuanxiang, Deng, Yue, Li, Jingzhi
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01-06-2024; Taylor & Francis Ltd
• Subjects: Accidents, Traffic; Adult; Bicycles; Bicycling; China; Cluster Analysis; Clustering; Clusters; Crashes; Datasets; Electric bicycles; Environment Design; Female; heterogeneity; Humans; latent class clustering; Male; Middle Aged; Motor scooters; Optimization; partial proportional odds model; riding behaviours; Road conditions; Roads; Vehicle-SSEB crashes; Young Adult; China; Vehicle-SSEB crashes; partial proportional odds model; road conditions; latent class clustering; heterogeneity; riding behaviours
• ISSN: 1745-7300; 1745-7319
• DOI: 10.1080/17457300.2023.2279960

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.