Multinomial logistic regression for prediction of vulnerable road users risk injuries based on spatial and temporal assessment

Abstract

Urban area's rapid growth often leads to adverse effects such as traffic congestion and increasing accident risks due to the expansion in transportation systems. In the frame of smart cities, active modes are expected to be promoted to improve living conditions. To achieve this goal, it is necessary to reduce the number of vulnerable road users (VRUs) injuries. Considering injury severity levels from crashes involving VRUs, this article seeks spatial and temporal patterns between cities and presents a model to predict the likelihood of VRUs to be involved in a crash. Kernel Density Estimation was applied to identify blackspots based on injury severity levels. A Multinomial Logistic Regression model was developed to identify statistically significant variables to predict the occurrence of these crashes. Results show that target spatial and temporal variables influence the number and severity of crashes involving VRUs. This approach can help to enhance road safety policies.