A joint-probability approach to crash prediction models

Accident Analysis and Prevention

Volumn 43, Issue 3, 2011, Pages 1160-1166

  Pei, Xin ^a   Wong, S C ^a   Sze, N N ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Crash frequency; Crash severity; Full Bayesian method; Joint probability; Markov chain Monte Carlo (MCMC) approach

Indexed keywords

CRASH FREQUENCY; CRASH SEVERITY; FULL BAYESIAN METHOD; JOINT PROBABILITY; MARKOV CHAIN MONTE CARLO (MCMC) APPROACH;

BAYESIAN NETWORKS; DATA STRUCTURES; ESTIMATION; FORECASTING; MARKOV PROCESSES; MATHEMATICAL MODELS; REGRESSION ANALYSIS; RESEARCH;

MONTE CARLO METHODS;

ARTICLE; BAYES THEOREM; CLASSIFICATION; HUMAN; INJURY; MONTE CARLO METHOD; MORTALITY; PROBABILITY; SAFETY; STATISTICAL MODEL; STATISTICS; SURVIVAL; TRAFFIC ACCIDENT;

ACCIDENTS, TRAFFIC; BAYES THEOREM; HUMANS; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; SAFETY; SURVIVAL ANALYSIS; WOUNDS AND INJURIES;

EID: 79952438774     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2010.12.026     Document Type: Article

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