Investigating macro-level hotzone identification and variable importance using big data: A random forest models approach

Neurocomputing

Volumn 181, Issue , 2016, Pages 53-63

  Jiang, Ximiao ^a   Abdel Aty, Mohamed ^b   Hu, Jia ^a   Lee, Jaeyoung ^b  

^a Federal Highway Administration Virginia Division   (United States)

^b University of Central Florida   (United States)

Author keywords

Big data; Connected Vehicle; Hotzone identification; Random forest; Variable importance; Wilcoxon test

Indexed keywords

ACCIDENT PREVENTION; CRASHWORTHINESS; DECISION TREES; HIGHWAY ADMINISTRATION; HIGHWAY PLANNING; PEDESTRIAN SAFETY; ROADS AND STREETS; SAFETY ENGINEERING; TRAFFIC CONTROL; URBAN PLANNING; WIDE AREA NETWORKS;

CROSS-VALIDATION METHODS; MACROSCOPIC VARIABLES; RANDOM FORESTS; ROAD INFRASTRUCTURES; TRAFFIC ANALYSIS ZONES; VARIABLE IMPORTANCES; WILCOXON TEST; YOUNG DRIVERS' BEHAVIOR;

BIG DATA;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BICYCLE; CONTROLLED STUDY; DATA ANALYSIS; DRIVER; FEASIBILITY STUDY; MATHEMATICAL MODEL; PEDESTRIAN; PRIORITY JOURNAL; RANDOM FOREST; RANK SUM TEST; SCHOOL; SOCIOECONOMICS; TRAFFIC ACCIDENT; TRAFFIC SAFETY; UNITED STATES; URBAN AREA; VALIDATION STUDY;

EID: 84958936288     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.08.097     Document Type: Article

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