Citation

Wang S, Yu J, Ma J. Travel Behav. Soc. 2023; 33: e100636.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.tbs.2023.100636

PMID

unavailable

Abstract

This study identifies the heterogeneous effects of road characteristics on motorcycle-involved crash severities by using the data of 11,611 crashes in London from 2017 to 2020. Latent class clustering (LCC) is employed to identify the typical classes and features of motorcycle-involved crashes, considering the driver, vehicle, and environmental characteristics. The partial proportion odds (PPO) model is then applied to discuss the heterogeneous effects of road characteristics on motorcycle-involved crash severities for each class. Parameter evaluations and marginal effects are given to interpret the results better and discuss the effects of road characteristics.

RESULTS suggest that four latent classes developed by LCC are more insightful and effective in identifying the heterogeneous effects of road characteristics on motorcycle-involved crash severity. Differences between the four classes do exist, confirming that significant heterogeneity exists both within and between the four classes. Many road characteristics are found to contribute to crash severities, such as junction controls, road obstacles, restricted lanes, and speed limits. Notably, junctions with proper controls can greatly alleviate serious injury, especially junctions with gesture control (marginal effects for fatal injury − 35.33%). Road obstacles have significant impacts on aggregating serious injury, and facility-related obstacles (marginal effects for serious injury + 19.94%) are more severe than non-facility obstacles (marginal effects for serious injury + 13.37%). These findings offer critical policy implications to enhance motorcycle-involved crash safety by improving road characteristics.

Language: en

Keywords

Heterogeneous effects; Latent class clustering; Motorcycle-involved crash severity; Partial proportional odds model; Road characteristics