Using data mining techniques to predict the severity of bicycle crashes

Accident Analysis and Prevention

Volumn 101, Issue , 2017, Pages 44-54

  Prati, Gabriele ^a   Pietrantoni, Luca ^a   Fraboni, Federico ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

Bicycle crash; Cycling; Data mining; Decision tree; Fatality; Injury; Safety

Indexed keywords

ACCIDENT PREVENTION; BAYESIAN NETWORKS; BICYCLES; DECISION TREES; HIGHWAY ACCIDENTS; MOTOR TRANSPORTATION; PAVEMENTS; SPORTING GOODS; TREES (MATHEMATICS);

BICYCLE CRASHES; CRASH CHARACTERISTICS; CYCLING; DECISION TREE TECHNIQUES; ENVIRONMENTAL FACTORS; FATALITY; INJURY; OBSERVATIONAL STUDY;

DATA MINING;

ADOLESCENT; ADULT; AGED; BAYES THEOREM; CHILD; CYCLING; DATA MINING; DECISION TREE; EPIDEMIOLOGY; FACTUAL DATABASE; FEMALE; HUMAN; INFANT; INJURIES; ITALY; MALE; MIDDLE AGED; MORTALITY; NEWBORN; PRESCHOOL CHILD; RISK FACTOR; STATISTICS AND NUMERICAL DATA; TRAFFIC ACCIDENT; WEATHER; YOUNG ADULT;

ACCIDENTS, TRAFFIC; ADOLESCENT; ADULT; AGED; BAYES THEOREM; BICYCLING; CHILD; CHILD, PRESCHOOL; DATA MINING; DATABASES, FACTUAL; DECISION TREES; FEMALE; HUMANS; INFANT; INFANT, NEWBORN; ITALY; MALE; MIDDLE AGED; RISK FACTORS; WEATHER; YOUNG ADULT;

EID: 85011915764     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2017.01.008     Document Type: Article

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