A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis

Accident Analysis and Prevention

Volumn 99, Issue , 2017, Pages 51-65

  Zeng, Ziqiang ^a,b,c   Zhu, Wenbo ^c   Ke, Ruimin ^c   Ash, John ^c   Wang, Yinhai ^c   Xu, Jiuping ^b   Xu, Xinxin ^a,b  

^a CHENGDU UNIVERSITY   (China)

^b SICHUAN UNIVERSITY   (China)

^c University of Washington   (United States)

Author keywords

Crash frequency; Crash severity; Generalized nonlinear model; Mixed multinomial logit; Traffic safety

Indexed keywords

CRASHWORTHINESS; NONLINEAR SYSTEMS;

CRASH FREQUENCY; CRASH SEVERITY; GENERALIZED NONLINEAR MODEL; MULTINOMIAL LOGIT; TRAFFIC SAFETY;

HIGHWAY ACCIDENTS;

BAYES THEOREM; CAR DRIVING; ENVIRONMENTAL PLANNING; HUMAN; NONLINEAR SYSTEM; REGRESSION ANALYSIS; SAFETY; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TRAFFIC ACCIDENT; WASHINGTON; WEATHER;

ACCIDENTS, TRAFFIC; AUTOMOBILE DRIVING; BAYES THEOREM; ENVIRONMENT DESIGN; HUMANS; LOGISTIC MODELS; NONLINEAR DYNAMICS; REGRESSION ANALYSIS; SAFETY; WASHINGTON; WEATHER;

EID: 84995967806     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2016.11.008     Document Type: Article

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