title: Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation.
abstract: There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.
authors: Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street, Honolulu, HI 96822, United States.
authors: Cong
authors: C
authors: Chen
authors: Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street, Honolulu, HI 96822, United States. Electronic address: guohui@hawaii.edu.
authors: Guohui
authors: G
authors: Zhang
authors: Department of Civil & Environmental Engineering, University of Utah, 110 Central Campus Drive, Suite 2000, Salt Lake City, UT 84112, United States.
authors: Xiaoyue Cathy
authors: XC
authors: Liu
authors: School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China.
authors: Yusheng
authors: Y
authors: Ci
authors: Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China.
authors: Helai
authors: H
authors: Huang
authors: Traffic Operations Division, Texas Department of Transportation, Austin, TX, 78717, USA.
authors: Jianming
authors: J
authors: Ma
authors: Beijing Transportation Engineering Key Laboratory, Beijing University of Technology, Beijing, 100124, China.
authors: Yanyan
authors: Y
authors: Chen
authors: Transportation Research Center, Beijing University of Technology, Beijing, 100124, China.
authors: Hongzhi
authors: H
authors: Guan
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D002681:China; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012424:Rural Population; D012449:Safety / Q000706:statistics & numerical data*; D012622:Seat Belts / Q000706:statistics & numerical data
keywords: Bayesian inference; Driver injury severity; Hierarchical model; Rural interstate highway; Traffic crash