Data-Mining Techniques for Exploratory Analysis of Pedestrian Crashes

Exploratory analysis was made of data from pedestrian crashes to detect interdependence and dissimilarities between crash patterns and to provide insight for the development of safety improvement strategies focused on pedestrians. Data-mining techniques, such as classification trees and association rules, were used on data related to 56,014 pedestrian crashes that occurred in Italy from 2006 to 2008. Crash severity was the response variable most sensitive to crash patterns. The most influential crash patterns were road type, pedestrian age, lighting conditions, vehicle type, and interactions between these patterns. Notable results included associations between fatal crashes and rural areas, urban provincial and national roads, pedestrians older than 75 years, nighttime conditions, pedestrians older than 65 years in nighttime crashes, drivers' young age and male gender in nighttime crashes, and truck involvement. To mitigate the fatal crash patterns identified by the classification trees and association rules, several measures are suggested for implementation. Results of the study are consistent with results of previous studies that used other analytic techniques, such as probabilistic models of crash injury severity. The data-mining techniques used in the study were able to detect interdependencies among crash characteristics. The use of classification trees and association rules, however, must be seen not as an attempt to supplant other techniques, but as a complementary method that can be integrated into other safety analyses.