Analyse vehicle–pedestrian crash severity at intersection with data mining techniques

Pedestrians are vulnerable road users subject to severer injuries and higher fatality risk in motor vehicle crashes due to limited protection. An important portion of vehicle–pedestrian crashes occurred at intersections due to the complex movements of various types of road users and the conflicts among them. To address the safety concern, this paper investigates the contributing factors to the severity of vehicle–pedestrian crashes at intersections based on a 3-year crash dataset of Hong Kong. For the crash severity modelling process, the crash dataset is mass and complicated. To tackle the class imbalance issue of the crash severity level, data resampling method is firstly applied. Then, various data mining algorithms, namely, classification and regression tree (CART) model, gradient boosting (GB) model, random forest (RF) model, artificial neural network (ANN) model and support vector machine (SVM) model, have been applied. The performance of these models have also been compared with the logistic regression model commonly applied in the literature. The ANN model which has the best performance is selected to determine the most significant contributing factors to the fatal and severe crashes, and the marginal effects of these factors are also analysed. Results show that the likelihood of fatal and severe vehicle–pedestrian crashes at intersections increase when there is light rain and where the junction control type is traffic signal and no control. On the other hand, the crash severity tends to decrease when the weather condition is clear, the light condition is daylight and dark, and in the districts of Kwun Tong, Kowloon City, Central and Western, and Sham Shui Po. Based on the results, policy implications and counter-measures on reducing the fatal and severe vehicle–pedestrian crashes at intersections have been recommended.