Automated and Explainable Artificial Intelligence to Enhance Prediction of Pedestrian Injury Severity

This study used a detailed explainable AI for automatic machine learning, AutoGluon, to predict pedestrian injury severity using data collected over five years (2016-2021) in Louisiana. The final dataset includes forty variables related to pedestrian characteristics, environmental circumstances, and vehicle specifications. Pedestrian injury severity was divided into three categories: fatal, injury, and no injury. The novelty of this approach lies in the application of explainable AI (XAI), specifically SHAP (SHapley Additive exPlanations) values, to interpret the AutoML model's predictions. This combination not only addressed the opaqueness typically associated with AI "black box" models but also illuminated the critical variables influencing pedestrian injury severity outcomes. The results revealed that the weighted ensemble model emerged as top performers, showcasing high accuracy with minimal prediction times, demonstrating the potential of ensemble methods in improving prediction outcomes by integrating the strengths of various individual models. Furthermore, the global and local explainability analyses provided by SHAP values afforded us an in-depth understanding of the variables influencing pedestrian injury severity. This dual-level explanation offered valuable insights into the complex dynamics at play, ranging from pedestrian impairment and driver condition to environmental variables like lighting and weather conditions. These findings underscore the importance of specific variables in crash outcomes, offering actionable intelligence for targeted interventions.