Using hierarchical Bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data

Accident Analysis and Prevention

Volumn 62, Issue , 2014, Pages 161-167

  Yu, Rongjie ^a   Abdel Aty, Mohamed ^b  

^a TONGJI UNIVERSITY   (China)

^b University of Central Florida   (United States)

Author keywords

Bayesian inference; Binary probit model; Crash injury severity; Hierarchical probit model; Random effects

Indexed keywords

BAYESIAN INFERENCE; CRASH INJURY SEVERITY; EXPLANATORY VARIABLES; PROBIT MODELS; RANDOM EFFECTS; REAL-TIME TRAFFIC DATUM; SIGNIFICANT VARIABLES; UNOBSERVED HETEROGENEITY; HIERARCHICAL BAYESIAN; HIGH SPEED;

AUTOMATIC VEHICLE IDENTIFICATION; BAYESIAN NETWORKS; INFERENCE ENGINES; LOSSES; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; RANDOM PROCESSES;

HIGHWAY ACCIDENTS;

ARTICLE; BAYES THEOREM; BAYESIAN INFERENCE; BINARY PROBIT MODEL; CAR DRIVING; CRASH INJURY SEVERITY; ENVIRONMENTAL PLANNING; HIERARCHICAL PROBIT MODEL; HUMAN; INJURY SCALE; MOTION; RANDOM EFFECTS; RISK FACTOR; STATISTICAL MODEL; STATISTICS; TRAFFIC ACCIDENT; UNITED STATES; WEATHER;

BAYESIAN INFERENCE; BINARY PROBIT MODEL; CRASH INJURY SEVERITY; HIERARCHICAL PROBIT MODEL; RANDOM EFFECTS;

ACCIDENTS, TRAFFIC; AUTOMOBILE DRIVING; BAYES THEOREM; COLORADO; ENVIRONMENT DESIGN; FLORIDA; HUMANS; MODELS, STATISTICAL; MOTION; RISK FACTORS; TRAUMA SEVERITY INDICES; WEATHER;

EID: 84887001951     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2013.08.009     Document Type: Article

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