A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity

Accident Analysis and Prevention

Volumn 99, Issue , 2017, Pages 184-191

  Zeng, Qiang ^a   Wen, Huiying ^a   Huang, Helai ^b   Pei, Xin ^c   Wong, S C ^d  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b CENTRAL SOUTH UNIVERSITY   (China)

^c TSINGHUA UNIVERSITY   (China)

^d UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Bayesian inference; Crash rate by severity; Multivariate Tobit model; Random parameters

Indexed keywords

BAYESIAN NETWORKS; HEALTH RISKS; INFERENCE ENGINES; PARAMETER ESTIMATION; ROADS AND STREETS; TRAFFIC CONTROL; TRANSPORTATION;

BAYESIAN INFERENCE; CRASH RATE; DEVIANCE INFORMATION CRITERION; HETEROGENEOUS EFFECTS; MULTIVARIATE TOBIT; RANDOM PARAMETERS; TRAFFIC INFORMATION SYSTEMS; UNOBSERVED HETEROGENEITY;

HIGHWAY ACCIDENTS;

BAYES THEOREM; CLASSIFICATION; EPIDEMIOLOGY; FEMALE; HONG KONG; HUMAN; INJURY; INJURY SCALE; MALE; RISK FACTOR; SAFETY; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TRAFFIC ACCIDENT;

ACCIDENTS, TRAFFIC; BAYES THEOREM; FEMALE; HONG KONG; HUMANS; MALE; MODELS, STATISTICAL; RISK FACTORS; SAFETY; TRAUMA SEVERITY INDICES; WOUNDS AND INJURIES;

EID: 85001958596     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2016.11.018     Document Type: Article

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