Utilizing Structural Equation Modeling and Segmentation Analysis in Real-time Crash Risk Assessment on Freeways
- Resource Type
- Article
- Authors
- Chengcheng Xu; Dawei Li; Zhibin Li; Wei Wang; Pan Liu
- Source
- KSCE Journal of Civil Engineering, 22(7), pp.2569-2577 Jul, 2018
- Subject
- 토목공학
- Language
- English
- ISSN
- 1976-3808
1226-7988
The study aimed to utilize Structural Equation Modeling (SEM) and K-means clustering for predicting real-time crash risks onfreeways. The SEM was used to transform a number of correlated traffic variables into four independent latent traffic factors, and toestablish the interrelationships among the traffic variables and crash risks. The segmentation analysis based on K-means clusteringwas then conducted to investigate the main traffic factors affecting crash risks in various traffic regimes. It was found that: (a) Themeasurement equations in SEM can effectively account for the correlations among traffic variables by transforming numerouscorrelated traffic variables into several latent traffic variables; (b) The SEM can both capture the direct and indirect effects of trafficflow variables on crash risks. This promotes a better understanding how traffic conditions affect crash risks; (c) The SEM producesmore accurate estimates of crash risks than existing modeling technique. It can increase the crash prediction accuracy by an averageof 7.6% compared with the commonly used logistic regression; and (d) Segmentation analysis results suggested that the trafficfactors contributing to crash risks are various across different traffic regimes. The proactive crash prevention strategies for differenttraffic regimes were discussed based on the findings in the segmentation analysis