title: A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis.
abstract: The mixed multinomial logit (MNL) approach, which can account for unobserved heterogeneity, is a promising unordered model that has been employed in analyzing the effect of factors contributing to crash severity. However, its basic assumption of using a linear function to explore the relationship between the probability of crash severity and its contributing factors can be violated in reality. This paper develops a generalized nonlinear model-based mixed MNL approach which is capable of capturing non-monotonic relationships by developing nonlinear predictors for the contributing factors in the context of unobserved heterogeneity. The crash data on seven Interstate freeways in Washington between January 2011 and December 2014 are collected to develop the nonlinear predictors in the model. Thirteen contributing factors in terms of traffic characteristics, roadway geometric characteristics, and weather conditions are identified to have significant mixed (fixed or random) effects on the crash density in three crash severity levels: fatal, injury, and property damage only. The proposed model is compared with the standard mixed MNL model. The comparison results suggest a slight superiority of the new approach in terms of model fit measured by the Akaike Information Criterion (12.06 percent decrease) and Bayesian Information Criterion (9.11 percent decrease). The predicted crash densities for all three levels of crash severities of the new approach are also closer (on average) to the observations than the ones predicted by the standard mixed MNL model. Finally, the significance and impacts of the contributing factors are analyzed.
authors.affiliation: School of Tourism and Economic Management, Chengdu University, Chengdu, 610106, PR China; Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu, 610064, PR China; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA.
authors.forename: Ziqiang
authors.initials: Z
authors.lastname: Zeng
authors.affiliation: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA.
authors.forename: Wenbo
authors.initials: W
authors.lastname: Zhu
authors.affiliation: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA.
authors.forename: Ruimin
authors.initials: R
authors.lastname: Ke
authors.affiliation: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA.
authors.forename: John
authors.initials: J
authors.lastname: Ash
authors.affiliation: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA. Electronic address: yinhai@u.washington.edu.
authors.forename: Yinhai
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu, 610064, PR China.
authors.forename: Jiuping
authors.initials: J
authors.lastname: Xu
authors.affiliation: School of Tourism and Economic Management, Chengdu University, Chengdu, 610106, PR China; Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu, 610064, PR China.
authors.forename: Xinxin
authors.initials: X
authors.lastname: Xu
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D004779:Environment Design; D006801:Humans; D016015:Logistic Models; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D012449:Safety / Q000706:statistics & numerical data*; D014861:Washington; D014887:Weather
keywords: Crash frequency; Crash severity; Generalized nonlinear model; Mixed multinomial logit; Traffic safety