Incorporating travel time reliability in predicting the likelihood of severe crashes on arterial highways using non-parametric random-effect regression

作     者：Emmanuel Kidando Ren Moses Eren Erman Ozguven Thobias Sando 

作者机构：Department of Civil and Environmental Engineering FAMU-FSU College of Engineering Florida State University Tallahassee FL 32310 USA Department of Civil and Environmental Engineering FAMU-FSU College of Engineering Florida A & M University Tallahassee FL 32310 USA School of Engineering University of North Florida Jacksonville FL 32256 USA 

出 版 物：《Journal of Traffic and Transportation Engineering(English Edition)》 (交通运输工程学报（英文版）)

年 卷 期：2019年第6卷第5期

页      面：470-481页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0813[工学-建筑学] 0814[工学-土木工程] 0823[工学-交通运输工程] 

基　　金：the Center for Accessibility and Safety for an Aging Population at Florida State University Florida A&M University University of North Florida for funding support in research 

主　　题：Travel time reliability Crash severity Non-parametric distributed random-effect Gaussian distributed random-effect Dirichlet process prior 

摘      要：Travel time reliability(TTR) modeling has gain attention among researchers’ due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time.Despite this significant effort,its impact on the severity of a crash is not well explored.This study analyzes the effect of TTR and other variables on the probability of the crash severity occurring on arterial roads.To address the unobserved heterogeneity problem,two random-effect regressions were applied;the Dirichlet random-effect(DRE)and the traditional random-effect(TRE) logistic regression.The difference between the two models is that the random-effect in the DRE is non-parametrically specified while in the TRE model is parametrically specified.The Markov Chain Monte Carlo simulations were adopted to infer the parameters’ posterior distributions of the two developed models.Using four-year police-reported crash data and travel speeds from Northeast Florida,the analysis of goodness-of-fit found the DRE model to best fit the data.Hence,it was used in studying the influence of TTR and other variables on crash severity.The DRE model findings suggest that TTR is statistically significant,at 95 percent credible intervals,influencing the severity level of a crash.A unit increases in TTR reduces the likelihood of a severe crash occurrence by 25 percent.Moreover,among the significant variables,alcohol/drug impairment was found to have the highest impact in influencing the occurrence of severe crashes.Other significant factors included traffic volume,weekends,speed,work-zone,land use,visibility,seatbelt usage,segment length,undivided/divided highway,and age.