Case Study: Modelling Pedestrian Crashes in Melbourne

Outline of the Research

Traffic accidents involving pedestrians and vehicles are a significant concern for cities, because of the considerable disparity in their respective vulnerabilities; pedestrians are four times more likely to be injured in a traffic accident than other users, and 23 times more likely to be killed. In Melbourne, there are over 1000 vehicle-pedestrian crashes every year, many of which lead to serious injury, or loss of life. Consequently, understanding the spatial and temporal patterns of pedestrian crashes is central to being able to prevent the events, and increase the safety of the most vulnerable road users. Alireza Toran pour, Dr. Sarah Moridpour, and Dr. Richard Tay from RMIT University, and Professor Abbas Rajabifard from the University of Melbourne

About 1,100 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 occurring between 19:00 PM and 6:00 AM are more severe than other times. Moreover, spatial correlation shows that there are positive correlation between time and location of crashes. Kernel Density Estimation (KDE) is applied to explore the spatial distribution of vehicle-pedestrian crashes. KDE results show that most vehicle-pedestrian crashes between 19:00 PM and 6:00 AM occur around hotels, clubs and bars. Safety measures should be applied around these areas to assist in preventing and reducing the severity of vehicle-pedestrian crashes.

How AURIN was used

Impacts of the Research