Exploring the factors affecting injury severity in highway and non-highway crashes in Bangladesh applying machine learning and SHAP

To create effective preventive measures and targeted interventions, it is crucial to comprehend the contributing factors to the crash and quantify how they affect the injury, especially in least-developed countries. However, highway and non-highway crashes are linked to having distinguished characteristics, road-specific interventions, and data granularity. Combining all sorts of crashes into a single model may offer fewer insights than one would anticipate when building safety countermeasures. This research compares CART, RF, GBM, XGBoost, LightGBM, CatBoost, and AdaBoost and effectively simulates the complex relationship between collision injury severity and risk factors for both highway and non-highway crashes. Additionally, the Shapley Additive exPlanation (SHAP) framework is presented to explain the contribution of each risk factor from the output of the most appropriate classifier, thereby assisting in the construction of safety countermeasures and crash modification factors. GBM classifier was found to be the best classifier in terms of G-mean and AUC scores for both highway and non-highway models. Global SHAP values show that the type of collision, followed by the vehicle type, the vehicle involved, and road division, are the highest contributing factors for injury severity in highway crashes. For injury severity in non-highway crashes, the most important factors are the type of collision, followed by road division, vehicle type, and location type. Policy implications based on the study's findings have been suggested to develop successful preventive strategies and focused interventions. The study concludes by discussing the scope of future studies.