Crash injury severity analysis using Bayesian ordered probit models

Journal of Transportation Engineering

Volumn 135, Issue 1, 2009, Pages 18-25

  Xie, Yuanchang ^a   Zhang, Yunlong ^b   Liang, Faming ^c  

^a SOUTH CAROLINA STATE UNIVERSITY   (United States)

^b Department of Electrical and Computer Engineering   (United States)

^c TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Bayesian analysis; Injuries; Monte Carlo method; Traffic accidents

Indexed keywords

ACCIDENTS; AUTOMOBILE DRIVERS; BAYESIAN NETWORKS; BLOWOUT PREVENTERS; HIGHWAY ACCIDENTS; INFERENCE ENGINES; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; MONTE CARLO METHODS; PARAMETER ESTIMATION; PILES;

BAYESIAN; BAYESIAN ANALYSIS; BAYESIAN INFERENCES; COMPARISON RESULTS; CRASH INJURY SEVERITIES; DATA SETS; FLEXIBLE FRAMEWORKS; INJURIES; INJURY SEVERITIES; LARGE SAMPLES; MODEL PERFORMANCES; MULTINOMIAL LOGIT; NESTED LOGIT MODELS; ORDERED PROBIT MODELS; PREDICTION PERFORMANCES; PRIOR KNOWLEDGES; SAMPLE DATUMS; SAMPLE SIZES; STATISTICAL MODELS; TRAFFIC ACCIDENTS; TRAFFIC SAFETIES; TWO TYPES; VEHICLE TYPES;

MODAL ANALYSIS;

ACCIDENT; BAYESIAN ANALYSIS; DATA SET; INJURY; MONTE CARLO ANALYSIS; MOTORWAY; PARAMETERIZATION; PERFORMANCE ASSESSMENT; PREDICTION; RESEARCH WORK; TRANSPORTATION SAFETY;

EID: 57949096181     PISSN: 0733947X     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)0733-947X(2009)135:1(18)     Document Type: Article

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