Using data mining techniques to predict the severity of bicycle crashes.
To investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes.
['Prati|Gabriele|G|', 'Pietrantoni|Luca|L|', 'Fraboni|Federico|F|']
D000063:Accidents, Traffic D000293:Adolescent D000328:Adult D000368:Aged D001499:Bayes Theorem D001642:Bicycling D002648:Child D002675:Child, Preschool D057225:Data Mining D016208:Databases, Factual D003663:Decision Trees D005260:Female D006801:Humans D007223:Infant D007231:Infant, Newborn D007558:Italy D008297:Male D008875:Middle Aged D012307:Risk Factors D014887:Weather D055815:Young Adult
Data mining Bicycle crash Decision tree Cycling Fatality Injury Safety