Investigating injury severities of motorcycle riders : A two-step method integrating latent class cluster analysis and random parameters logit model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

6 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)316-326
Journal / PublicationAccident Analysis and Prevention
Volume131
Online published25 Jul 2019
Publication statusPublished - Oct 2019

Abstract

Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015–2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders’ injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety.

Research Area(s)

  • Injury severity analysis, Latent class clustering, Motorcycle crashes, Random parameters logit model, Unobserved heterogeneity

Citation Format(s)

Investigating injury severities of motorcycle riders : A two-step method integrating latent class cluster analysis and random parameters logit model. / Chang, Fangrong; Xu, Pengpeng; Zhou, Hanchu; Chan, Alan H.S.; Huang, H.

In: Accident Analysis and Prevention, Vol. 131, 10.2019, p. 316-326.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal