Exploring the heterogeneities of factors affecting e-bicyclist severities in delivery e-scooter crashes

Abstract

Delivery e-bicyclists are vulnerable traffic participants, and delivery e-scooter crashes annually cause great losses. There are limited empirical analyses performed to characterize the endogenous and exogenous influences of potential factors on delivery e-scooter crash severities. This study focused on identifying the heterogeneous impacts of contributing factors. Based on 3064 police-reported delivery e-scooter crashes, different potential factors selected from seven perspectives were examined. Combining the latent class clustering analysis and the partial proportional odds model, an unsupervised learning approach was developed to modeling the heterogeneities as well as the corresponding marginal effects. According to the AIC, BIC, and entropy-based approach, the most appropriate number of clusters was identified as three. The model results revealed that the top six factors significantly influencing the injury severities were running red light, reverse riding, speeding, bike lane, changing lane, and e-bicyclist age. Their maximum absolute values of the marginal effects were more than 29%. The sub-models owned a better goodness of fit in characterizing heterogeneities and avoiding the model bias. It was easy to find the remarkable heterogeneities within each factor and across different clusters. The findings could provide insightful guidance and effective recommendations for alleviating the crash losses and promoting rider safety.