A Hybrid Latent Class Analysis Modeling Approach to Analyze Urban Expressway Crash Risk.
Crash risk analysis is rising as a hot research topic as it could reveal the relationships between traffic flow characteristics and crash occurrence risk, which is beneficial to understand crash mechanisms which would further refine the design of Active Traffic Management System (ATMS). However, the majority of the current crash risk analysis studies have ignored the impact of geometric characteristics on crash risk estimation while recent studies proved that crash occurrence risk was affected by the various alignment features. In this study, a hybrid Latent Class Analysis (LCA) modeling approach was proposed to account for the heterogeneous effects of geometric characteristics. Crashes were first segmented into homogenous subgroups, where the optimal number of latent classes was identified based on bootstrap likelihood ratio tests. Then, separate crash risk analysis models were developed using Bayesian random parameter logistic regression technique; data from Shanghai urban expressway system were employed to conduct the empirical study. Different crash risk contributing factors were unveiled by the hybrid LCA approach and better model goodness-of-fit was obtained while comparing to an overall total crash model. Finally, benefits of the proposed hybrid LCA approach were discussed.
['Yu|Rongjie|R|', 'Wang|Xuesong|X|', 'Abdel-Aty|Mohamed|M|']
D000063:Accidents, Traffic D001499:Bayes Theorem D002681:China D004779:Environment Design D006801:Humans D016015:Logistic Models D018570:Risk Assessment D012307:Risk Factors D014505:Urban Population
Latent class analysis Crash risk analysis Bayesian random parameter model Unobserved heterogeneity