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Due to the stochastic nature of events, predicting the duration of a trafficincident presents a formidable challenge. Accurate duration estimation canresult in substantial advantages for commuters in selecting optimal routes andfor traffic management personnel in addressing non-recurring congestion issues.In this study, we gathered accident duration, road conditions, andmeteorological data from a database of traffic accidents to check thefeasibility of a traffic accident duration pipeline without accident contextualinformation data like accident severity and textual description. Multiplemachine learning models were employed to predict whether an accident's impacton road traffic would be of a short-term or long-term nature, and thenutilizing a bimodal approach the precise duration of the incident's effect wasdetermined. Our binary classification random forest model distinguished betweenshort-term and long-term effects with an 83% accuracy rate, while the LightGBMregression model outperformed other machine learning regression models withMean Average Error (MAE) values of 26.15 and 13.3 and RMSE values of 32.91 and28.91 for short and long-term accident duration prediction, respectively. Usingthe optimal classification and regression model identified in the precedingsection, we then construct an end-to-end pipeline to incorporate the entireprocess. The results of both separate and combined approaches were comparablewith previous works, which shows the applicability of only using staticfeatures for predicting traffic accident duration. The SHAP value analysisidentified weather conditions, wind chill and wind speed as the mostinfluential factors in determining the duration of an accident.

A Bi-level Framework for Traffic Accident Duration Prediction: Leveraging Weather and Road Condition Data within a Practical Optimum Pipeline