Spatial and temporal distribution of pedestrian crashes in Melbourne metropolitan area

Abstract

About 1100 vehicle-pedestrian crashes occur in Melbourne metropolitan area every year. Identifying the temporal and spatial patterns of pedestrian injuries is essential to enhance the safety of these vulnerable road users. In this paper, Decision Tree (DT) and interactive DT are applied to identify the influence of temporal, spatial and personal characteristics on vehicle-pedestrian crash severity. DT is a simple but powerful form of data analyses using machine learning technique. Result of DT indicates that time of crash is the most significant variable in classifying and predicting the severity of vehicle-pedestrian crashes in Melbourne metropolitan area. According to this model, accidents..