Exploring driver injury severity patterns and causes in low visibility related single-vehicle crashes using a finite mixture random parameters model

Abstract Low visibility is consistently considered as a hazardous factor due to its potential leading to severe fatal crashes. However, unlike the other inclement weather conditions that have attracted extensive research interests, only a few studies have been conducted to investigate the impacts of risk factors on driver injury severity outcomes in low visibility related crashes. A three-year crash dataset including all low visibility related crashes from 2010 to 2012 in four South Central states, i.e., Arkansas, Louisiana, Texas, and Oklahoma, is adopted in this study. A finite mixture random parameters approach is developed to interpret both within-class and between-class unobserved heterogeneity among crash data. After a careful comparison, a two-class finite mixture random parameter model with normal distribution assumptions is selected as the final model. Estimation results show that three variables, including young (specific to injury, I), male (specific to serious injury and fatality, F), and large truck (specific to serious injury and fatality, F), are found to be normally distributed and have significant impacts on driver injury severities. Variables with fixed effects including rural, wet, 60 mph or higher, no statutory limit, dark, Sunday, curve, rollover, light truck, old, and drug/alcohol impaired also have significant influences on driver injury severities. This study provides an insightful understanding of the impacts of these variables on driver injury severity outcomes in low visibility related crashes, and a beneficial reference for developing countermeasures and strategies to mitigate driver injury severities under these conditions.

[1]  Gudmundur F. Ulfarsson,et al.  Bicyclist injury severities in bicycle-motor vehicle accidents. , 2007, Accident; analysis and prevention.

[2]  Mohammed A Quddus,et al.  Injury severity analysis of accidents involving young male drivers in Great Britain. , 2008, Journal of safety research.

[3]  K. Train.  Halton Sequences for Mixed Logit , 2000 .

[4]  Mahdi Pour-Rouholamin,et al.  Investigating the risk factors associated with pedestrian injury severity in Illinois. , 2016, Journal of safety research.

[5]  F. Mannering,et al.  The effects of road-surface conditions, age, and gender on driver-injury severities. , 2011, Accident; analysis and prevention.

[6]  Chandra R. Bhat,et al.  Unobserved heterogeneity and the statistical analysis of highway accident data , 2016 .

[7]  Chandra R. Bhat,et al.  A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity , 2014 .

[8]  Sudip Barua,et al.  Multivariate random parameters collision count data models with spatial heterogeneity , 2016 .

[9]  Fred L. Mannering,et al.  Latent Class Analysis of the Effects of Age, Gender, and Alcohol Consumption on Driver-Injury Severities , 2014 .

[10]  Fred L. Mannering,et al.  The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach , 2013 .

[11]  D. McFadden,et al.  URBAN TRAVEL DEMAND - A BEHAVIORAL ANALYSIS , 1977 .

[12]  Kiyoshi Yamaoka,et al.  Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetic equations , 1978, Journal of Pharmacokinetics and Biopharmaceutics.

[13]  Niranga Amarasingha,et al.  Gender differences of young drivers on injury severity outcome of highway crashes. , 2014, Journal of safety research.

[14]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[15]  Erdong Chen,et al.  Modeling safety of highway work zones with random parameters and random effects models , 2014 .

[16]  Fred L. Mannering,et al.  The temporal stability of factors affecting driver-injury severities in single-vehicle crashes: Some empirical evidence , 2015 .

[17]  Kevin J. Gaston,et al.  Local avian assemblages as random draws from regional pools , 2001 .

[18]  Dominique Lord,et al.  Comparing Three Commonly Used Crash Severity Models on Sample Size Requirements: Multinomial Logit, Ordered Probit, and Mixed Logit Models , 2014 .

[19]  Qiong Wu,et al.  Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways. , 2014, Accident; analysis and prevention.

[20]  Mohamed Abdel-Aty,et al.  A study on crashes related to visibility obstruction due to fog and smoke. , 2011, Accident; analysis and prevention.

[21]  Jean-Philippe Tarel,et al.  Vision Enhancement in Homogeneous and Heterogeneous Fog , 2012, IEEE Intelligent Transportation Systems Magazine.

[22]  Y. Zou,et al.  Analyzing different functional forms of the varying weight parameter for finite mixture of negative binomial regression models , 2014 .

[23]  Fred L. Mannering,et al.  The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity , 2014 .

[24]  Stephen P. Jenkins,et al.  Multivariate Probit Regression using Simulated Maximum Likelihood , 2003 .

[25]  Shauna L. Hallmark,et al.  Analysis of Occupant Injury Severity in Winter Weather Crashes: A Fully Bayesian Multivariate Approach , 2016 .

[26]  F. Mannering.  Temporal instability and the analysis of highway accident data , 2018 .

[27]  Fred L Mannering,et al.  Highway accident severities and the mixed logit model: an exploratory empirical analysis. , 2008, Accident; analysis and prevention.

[28]  Silvio Brusaferro,et al.  Risk factors for fatal road traffic accidents in Udine, Italy. , 2002, Accident; analysis and prevention.

[29]  Konstantina Gkritza,et al.  A latent class analysis of single-vehicle motorcycle crash severity outcomes , 2014 .

[30]  D. Weakliem.  A Critique of the Bayesian Information Criterion for Model Selection , 1999 .

[31]  Guohui Zhang,et al.  Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models. , 2018, Accident; analysis and prevention.

[32]  Sigal Kaplan,et al.  Analysis of factors associated with injury severity in crashes involving young New Zealand drivers. , 2014, Accident; analysis and prevention.

[33]  Fred L. Mannering,et al.  An empirical assessment of the effects of economic recessions on pedestrian-injury crashes using mixed and latent-class models , 2016 .

[34]  James G. Scott,et al.  Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data , 2016 .

[35]  Mohamed Abdel-Aty,et al.  Crash risk analysis during fog conditions using real-time traffic data. , 2017, Accident; analysis and prevention.

[36]  M. Abdel-Aty,et al.  Analysis of left-turn crash injury severity by conflicting pattern using partial proportional odds models. , 2008, Accident; analysis and prevention.

[37]  Kimberly J Adams,et al.  Disparity between state fish consumption advisory systems for methylmercury and US Environmental Protection Agency recommendations: A case study of the south central United States. , 2016, Environmental toxicology and chemistry.

[38]  Guohui Zhang,et al.  An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier. , 2016, Accident; analysis and prevention.

[39]  Chandra R. Bhat,et al.  A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling , 2014 .

[40]  F. Mannering,et al.  Determinants of bicyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances , 2017 .

[41]  I. Norros,et al.  The Palm distribution of traffic conditions and its application to accident risk assessment , 2016 .

[42]  Hoong Chor Chin,et al.  An analysis of motorcycle injury and vehicle damage severity using ordered probit models. , 2002, Journal of safety research.

[43]  Birsen Donmez,et al.  Associations of distraction involvement and age with driver injury severities. , 2015, Journal of safety research.

[44]  Xiaoyu Zhu,et al.  A comprehensive analysis of factors influencing the injury severity of large-truck crashes. , 2011, Accident; analysis and prevention.

[45]  Gudmundur F. Ulfarsson,et al.  The crash severity impacts of fixed roadside objects. , 2005, Journal of safety research.

[46]  Andrew P Tarko,et al.  Markov switching negative binomial models: an application to vehicle accident frequencies. , 2008, Accident; analysis and prevention.

[47]  Gudmundur F. Ulfarsson,et al.  A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. , 2010, Accident; analysis and prevention.

[48]  Haizhong Wang,et al.  Heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes. , 2016, Accident; analysis and prevention.

[49]  Michael D. Fontaine,et al.  Assessing Driver Speed Choice in Fog with the Use of Visibility Data from Road Weather Information Systems , 2016 .

[50]  Yu-Chiun Chiou,et al.  Incorporating spatial dependence in simultaneously modeling crash frequency and severity , 2014 .

[51]  Fred L. Mannering,et al.  Occupant injury severities in hybrid-vehicle involved crashes: A random parameters approach with heterogeneity in means and variances , 2017 .

[52]  Guohui Zhang,et al.  Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model. , 2016, Accident; analysis and prevention.

[53]  Sabyasachee Mishra,et al.  Analysis of injury severity of large truck crashes in work zones. , 2016, Accident; analysis and prevention.

[54]  Mohamed Abdel-Aty,et al.  Analysis of driver injury severity levels at multiple locations using ordered probit models. , 2003, Journal of safety research.

[55]  Kirolos Haleem,et al.  Contributing factors of crash injury severity at public highway-railroad grade crossings in the U.S. , 2015, Journal of safety research.

[56]  Mohamed Abdel-Aty,et al.  Real-time prediction of visibility related crashes , 2012 .

[57]  Christopher Schreiner,et al.  Reducing fog-related crashes on the Afton and Fancy Gap Mountain sections of I-64 and I-77 in Virginia , 2002 .

[58]  Liping Fu,et al.  A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings. , 2012, Accident; analysis and prevention.

[59]  Brendan J. Russo,et al.  Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model , 2014 .

[60]  Chandra R. Bhat,et al.  Analytic methods in accident research: Methodological frontier and future directions , 2014 .

[61]  Guohui Zhang,et al.  Risk factors affecting fatal bus accident severity: Their impact on different types of bus drivers. , 2016, Accident; analysis and prevention.

[62]  Simon Washington,et al.  Bayesian Latent Class Safety Performance Function for Identifying Motor Vehicle Crash Black Spots , 2016 .

[63]  Gudmundur F. Ulfarsson,et al.  Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender. , 2013, Accident; analysis and prevention.

[64]  Mohamed Abdel-Aty,et al.  A Hybrid Latent Class Analysis Modeling Approach to Analyze Urban Expressway Crash Risk. , 2017, Accident; analysis and prevention.

[65]  Geert Wets,et al.  Traffic accident segmentation by means of latent class clustering. , 2008, Accident; analysis and prevention.

[66]  C. Bhat.  Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences , 2003 .

[67]  R Elvik,et al.  Nilsson's Power Model connecting speed and road trauma: applicability by road type and alternative models for urban roads. , 2010, Accident; analysis and prevention.

[68]  Asad J. Khattak,et al.  What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors. , 2015, Accident; analysis and prevention.

[69]  Zong Tian,et al.  Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations. , 2015, Accident; analysis and prevention.

[70]  Chris Lee,et al.  Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario. , 2014, Accident; analysis and prevention.

[71]  Mohamed M Ahmed,et al.  Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models. , 2018, Accident; analysis and prevention.

[72]  Corinne Brusque,et al.  Drivers' phone use at red traffic lights: a roadside observation study comparing calls and visual-manual interactions. , 2015, Accident; analysis and prevention.

[73]  M. Hadji Hosseinlou,et al.  Analysis of the injury severity of crashes by considering different lighting conditions on two-lane rural roads. , 2016 .

[74]  Ahmet Tortum,et al.  Accident analysis with aggregated data: the random parameters negative binomial panel count data model , 2015 .

[75]  Bronwyn H Hall,et al.  Estimation and Inference in Nonlinear Structural Models , 1974 .

[76]  Mohan M. Trivedi,et al.  On surveillance for safety critical events: In-vehicle video networks for predictive driver assistance systems , 2015, Comput. Vis. Image Underst..

[77]  Liping Fu,et al.  Using a flexible multivariate latent class approach to model correlated outcomes: A joint analysis of pedestrian and cyclist injuries , 2017 .

[78]  J D Dawson,et al.  Driving under low-contrast visibility conditions in Parkinson disease , 2009, Neurology.