Predicting Crash-Related Incident Clearance Time on Louisiana’s Rural Interstate Using Ensemble Tree-Based Learning Methods

Traffic crashes contribute significantly to non-recurrent congestion, thereby increasing delays, congestion pollution, and other challenges. It is important to have tools that enable accurate prediction of incident duration to reduce delays. It is also necessary to understand factors that affect the duration of traffic crashes. This study developed three machine learning models, namely extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and a light gradient-boosting machine (LightGBM), to predict crash-related incident clearance time in Louisiana rural interstates and utilized Shapley additive explanations (SHAP) analysis to determine the influence of factors impacting it. Four ICT levels were defined based on 30 min intervals: short (0–30), medium (31–60), intermediate (61–90), and long (greater than 90). The results suggest that XGBoost outperforms CatBoost and LightGBM in the collective model’s predictive performance. It was found that different features significantly affect different ICT levels. The results indicate that crashes involving injuries, fatalities, heavy trucks, head-on collisions, roadway departure, and older drivers are the significant factors that influence ICT. The results of this study may be used to develop and implement strategies that lead to reduced incident duration and related challenges with long clearance times, providing actionable insights for traffic managers, transportation planners, and incident response agencies to enhance decision-making and mitigate the associated increases in congestion and secondary crashes.