Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models. (November 2018)

• Record Type: Journal Article
• Authors: Li, Zhenning; Chen, Cong; Ci, Yusheng; Zhang, Guohui; Wu, Qiong; Liu, Cathy; Qian, Zhen (Sean)
• Is Part Of: Accident analysis and prevention. Volume 120(2018)
• Page Start: 139
• Page End: 151
• Publication Date: 2018-11
• Subjects: Driver injury severity -- Intersection-related crash -- Cross-level interaction -- K-means cluster analysis -- Hierarchical Bayesian model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.08.009
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: This study examines driver injury severities in intersection-related crashes. A hybrid method of cluster analysis and hierarchical Bayesian model is developed. Risk compensation, unobserved heterogeneity, and data structure are all considered. This study provides insights on injury prevention in intersection-related crashes. Abstract: Traffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns in intersection-related crashes based on two-year crash data in New Mexico. Three clusters are defined by K-means cluster analysis based on weather and roadway environmental conditions in order to reveal drivers' risk compensation instability under diverse external environment. Hierarchical Bayesian random intercept models are developed for each of the three clusters as well as the whole dataset to identify the contributing factors on multilevel driver injury outcomes: property damage only (Level I), complaint of injury and visible injury (Level II), and incapacitating injury and fatality (Level III). Model comparison with an ordinary multinomial logistic model omitting crash data hierarchical features and cross-level interactions verifies the suitability and effectiveness of the proposed hybrid approach. Results show that a number of crash-level variables (time period, weather, light condition, area, and road grade), vehicle/driver-level variables (traffic controls, vehicle action, vehicle type, seatbelt used, driver age, drug/alcohol impaired, and driver age) along with some cross-level interactions (i.e., left turn and night, drug and dark) impose significantly influence driver injury severity. This study provides insightful understandings of the effects of these variables on driver injury severity in intersection-related crashes and beneficial references for developing effective countermeasures for severe crash prevention.