Exploring driver injury severity patterns and causes in low visibility related single-vehicle crashes using a finite mixture random parameters model
暂无分享,去创建一个
Panos D. Prevedouros | Guohui Zhang | Qiong Wu | Zhenning Li | Cong Chen | David T. Ma | Cathy Liu | Qiong Wu | Guohui Zhang | P. Prevedouros | Zhenning Li | Cong Chen | Cathy Liu
[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.