title: Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery.
abstract: Aim of the study was the analysis of powered two-wheeler (PTW) crashes in Italy in order to detect interdependence as well as dissimilarities among crash characteristics and provide insights for the development of safety improvement strategies focused on PTWs. At this aim, data mining techniques were used to analyze the data relative to the 254,575 crashes involving PTWs occurred in Italy in the period 2006-2008. Classification trees analysis and rules discovery were performed. Tree-based methods are non-linear and non-parametric data mining tools for supervised classification and regression problems. They do not require a priori probabilistic knowledge about the phenomena under studying and consider conditional interactions among input data. Rules discovery is the identification of sets of items (i.e., crash patterns) that occur together in a given event (i.e., a crash in our study) more often than they would if they were independent of each other. Thus, the method can detect interdependence among crash characteristics. Due to the large number of patterns considered, both methods suffer from an extreme risk of finding patterns that appear due to chance alone. To overcome this problem, in our study we randomly split the sample data in two data sets and used well-established statistical practices to evaluate the statistical significance of the results. Both the classification trees and the rules discovery were effective in providing meaningful insights about PTW crash characteristics and their interdependencies. Even though in several cases different crash characteristics were highlighted, the results of the two the analysis methods were never contradictory. Furthermore, most of the findings of this study were consistent with the results of previous studies which used different analytical techniques, such as probabilistic models of crash injury severity. Basing on the analysis results, engineering countermeasures and policy initiatives to reduce PTW injuries and fatalities were singled out. The simultaneous use of classification trees and association discovery must not, however, be seen as an attempt to supplant other techniques, but as a complementary method which can be integrated into other safety analyses.
authors.affiliation: University of Naples Federico II, Department of Transportation Engineering Luigi Tocchetti, Via Claudio 21, 80125 Naples, Italy. alfonso.montella@unina.it
authors.forename: Alfonso
authors.initials: A
authors.lastname: Montella
authors.forename: Massimo
authors.initials: M
authors.lastname: Aria
authors.forename: Antonio
authors.initials: A
authors.lastname: D'Ambrosio
authors.forename: Filomena
authors.initials: F
authors.lastname: Mauriello
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality / Q000517:prevention & control / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D002965:Classification; D057225:Data Mining / Q000379:methods; D003663:Decision Trees; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007558:Italy / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009049:Motorcycles; D012449:Safety / Q000706:statistics & numerical data*; D018709:Statistics, Nonparametric; D014947:Wounds and Injuries / Q000209:etiology / Q000401:mortality; D055815:Young Adult