Generalized nonlinear models for rear-end crash risk analysis

Accident Analysis and Prevention

Volumn 62, Issue , 2014, Pages 9-16

  Lao, Yunteng ^a   Zhang, Guohui ^b   Wang, Yinhai ^a   Milton, John ^c  

^a University of Washington   (United States)

^b MSC01 1070   (United States)

^c WASHINGTON STATE DEPARTMENT OF TRANSPORTATION   (United States)

Author keywords

Crash data modeling; Generalized linear model (GLM); Generalized nonlinear model (GNM); Quasi Poisson regression model; Traffic accidents

Indexed keywords

GENERALIZED LINEAR MODEL; GENERALIZED NONLINEAR MODEL; INDEPENDENT VARIABLES; LOGISTIC REGRESSIONS; MODEL INTERPRETATIONS; NON-LINEAR REGRESSION; NON-LINEAR RELATIONSHIPS; REGRESSION MODEL; REAR-END CRASHES;

MAXIMUM LIKELIHOOD; NONLINEAR SYSTEMS; POISSON DISTRIBUTION; REGRESSION ANALYSIS; RISK PERCEPTION;

HIGHWAY ACCIDENTS;

ARTICLE; CAR DRIVING; CRASH DATA MODELING; GENERALIZED LINEAR MODEL (GLM); GENERALIZED NONLINEAR MODEL (GNM); HUMAN; NONLINEAR SYSTEM; POISSON DISTRIBUTION; QUASI-POISSON REGRESSION MODEL; REGRESSION ANALYSIS; RISK ASSESSMENT; STATISTICAL MODEL; STATISTICS; TRAFFIC ACCIDENT; TRAFFIC ACCIDENTS;

CRASH DATA MODELING; GENERALIZED LINEAR MODEL (GLM); GENERALIZED NONLINEAR MODEL (GNM); QUASI-POISSON REGRESSION MODEL; TRAFFIC ACCIDENTS;

ACCIDENTS, TRAFFIC; AUTOMOBILE DRIVING; HUMANS; LINEAR MODELS; MODELS, STATISTICAL; NONLINEAR DYNAMICS; POISSON DISTRIBUTION; REGRESSION ANALYSIS; RISK ASSESSMENT;

EID: 84886383935     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2013.09.004     Document Type: Article

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