Red-light running violation prediction using observational and simulator data

Accident Analysis and Prevention

Volumn 96, Issue , 2016, Pages 316-328

  Jahangiri, Arash ^a   Rakha, Hesham ^a   Dingus, Thomas A ^a  

^a VIRGINIA TECH TRANSPORTATION INSTITUTE   (United States)

Author keywords

Driver violation; Machine learning; Observational data; Random forest; Red light running; Signalized intersection; Simulator data; Violation prediction

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMOBILE FRAMES; CRASHWORTHINESS; DECISION TREES; LEARNING SYSTEMS; SENSITIVITY ANALYSIS; SIMULATORS; TENSORS; TRAFFIC SIGNALS; VEHICLES;

DRIVER VIOLATION; OBSERVATIONAL DATA; RANDOM FORESTS; RED-LIGHT RUNNING; SIGNALIZED INTERSECTION;

FORECASTING;

ACCELERATION; ADOLESCENT; ADULT; CAR DRIVING; COMPUTER SIMULATION; DECELERATION; DECISION MAKING; FEMALE; HUMAN; INFORMATION PROCESSING; MALE; MIDDLE AGED; PREDICTIVE VALUE; PSYCHOLOGY; THEORETICAL MODEL; TIME FACTOR; UNITED STATES; VIOLENCE; YOUNG ADULT;

ACCELERATION; ADOLESCENT; ADULT; AUTOMOBILE DRIVING; COMPUTER SIMULATION; DANGEROUS BEHAVIOR; DATASETS AS TOPIC; DECELERATION; DECISION MAKING; FEMALE; HUMANS; MALE; MIDDLE AGED; MODELS, THEORETICAL; PREDICTIVE VALUE OF TESTS; TIME FACTORS; UNITED STATES; YOUNG ADULT;

EID: 84977614984     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2016.06.009     Document Type: Article

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