Machine Learning Approach for Ex-Post Evaluation of Road Traffic Collision Severity Trends

Abstract

Road traffic collisions (RTCs) have been a key concern because of their negative impact on road users’ safety and social aspects. To address this issue, many research efforts have been made, yet there is still very little known about the various key factors affecting RTC severity and the ability to feasibly provide the overall RTC severity classification trends. This study aimed to quantify the factors affecting the severity of RTCs using neural network search and sensitivity analysis. To this end, an ex-post evaluation framework that could systematically classify RTC severity trends in two stages was developed: stage 1 involved RTC severity classification based on a radial basis-function-driven machine learning model, and stage 2 utilized global sensitivity analysis to identify critical factors affecting RTC severity trends. It was shown that the radial basis function network models accurately predicted the RTC severity (with 77% accuracy) based on multi-contextual inputs and derived three key factors (road classification, number of vehicles involved, and speed limit) associated with the severity based on more than 12,000 RTC records collected over 6 years in the county of Cambridgeshire, UK. Generalized RTC severity trends associated with the results were also proposed and discussed as an ex-post evaluation. The outcomes of this study will help transportation authorities and engineers by serving as a benchmark and predictable reference to minimize the negative impact of potential RTCs on road users’ safety as well as social costs. © The Author(s) 2025.