Expressway crash risk prediction using back propagation neural network: A brief investigation on safety resilience

Abstract

This study presents the work in predicting crash risk on expressways with consideration of both the impact of safety critical events and traffic conditions. The traffic resilience theory is introduced to learn safety problems from the standpoint of 1) considering safety critical events, such as traffic violations, as the safety disturbances, and 2) considering safety resilience as the ability of the traffic, greatly associated with traffic conditions, to resist critical events turning into crashes. The concept of safety resilience was illustrated qualitatively through simulation experiments. Aimsun microsimulation software was used to simulate traffic conditions with safety critical events (vehicle violations, in this paper) involved based on the geometric design of the G15 Expressway in Shanghai. Based on data from the simulation experiment, a two-staged model was developed which classifies crash risk status into three types including no-risk, low-risk and high-risk status. Modeling approach that relies on the back propagation neural network method was applied. The performance of the model in prediction was validated through the Receiver Operating Characteristic (ROC) curve test. Results indicated that the model performed well in predicting crash risks in the simulated environment. After training the model, an extra simulation experiment involving six additional tests was conducted. Results show that the traffic resilience theory may work in explaining the relationship between traffic conditions, safety critical events and crash risk, which are the key elements in road safety field. The introduction of safety resilience may inspire further exploration on this topic in both research and practice. Meanwhile, the model can be used to predict and monitor risks on expressways in a potentially more precise way.