Prediction of roadway accident frequencies: Count regressions versus machine learning models

Scientia Iranica

Volumn 21, Issue 2, 2014, Pages 263-275

  Nassiri, H ^a   Najaf, P ^b   Mohamadian Amiri, A ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^b University of North Carolina at Charlotte   (United States)

Author keywords

Accident frequency prediction; Back propagation neural network; Negative binomialregression; Support vector machine; Zero inated negative binomial regression

Indexed keywords

BACKPROPAGATION; FORECASTING; MACHINE LEARNING; NEURAL NETWORKS; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; SUPPORT VECTOR MACHINES; TORSIONAL STRESS; TRAFFIC CONTROL;

ACCIDENT FREQUENCY; ACCIDENT PREDICTION MODEL; BACK PROPAGATION NEURAL NETWORKS; EFFECTIVE VARIABLES; MACHINE LEARNING MODELS; NEGATIVE BINOMIALREGRESSION; ROADWAY CHARACTERISTICS; SIGNIFICANT VARIABLES;

ACCIDENTS;

ACCIDENT; PREDICTION; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; TRAFFIC MANAGEMENT;

IRAN; MASHHAD; RAZAVI KHORASAN;

EID: 84906095523     PISSN: 10263098     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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