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

• URL: http://arxiv.org/abs/2311.00634v2
• Date: Fri, 3 Nov 2023 19:26:03 GMT
• Title: A Bi-level Framework for Traffic Accident Duration Prediction: Leveraging Weather and Road Condition Data within a Practical Optimum Pipeline
• Authors: Rafat Tabassum Sukonna, Soham Irtiza Swapnil
• Abstract summary: We gathered accident duration, road conditions, and meteorological data from a database of traffic accidents to check the feasibility of a traffic accident duration pipeline. Our binary classification random forest model distinguished between short-term and long-term effects with an 83% accuracy rate. The SHAP value analysis identified weather conditions, wind chill and wind speed as the most influential factors in determining the duration of an accident.
• Score: 0.5221459608786241
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the stochastic nature of events, predicting the duration of a traffic incident presents a formidable challenge. Accurate duration estimation can result in substantial advantages for commuters in selecting optimal routes and for traffic management personnel in addressing non-recurring congestion issues. In this study, we gathered accident duration, road conditions, and meteorological data from a database of traffic accidents to check the feasibility of a traffic accident duration pipeline without accident contextual information data like accident severity and textual description. Multiple machine learning models were employed to predict whether an accident's impact on road traffic would be of a short-term or long-term nature, and then utilizing a bimodal approach the precise duration of the incident's effect was determined. Our binary classification random forest model distinguished between short-term and long-term effects with an 83% accuracy rate, while the LightGBM regression model outperformed other machine learning regression models with Mean Average Error (MAE) values of 26.15 and 13.3 and RMSE values of 32.91 and 28.91 for short and long-term accident duration prediction, respectively. Using the optimal classification and regression model identified in the preceding section, we then construct an end-to-end pipeline to incorporate the entire process. The results of both separate and combined approaches were comparable with previous works, which shows the applicability of only using static features for predicting traffic accident duration. The SHAP value analysis identified weather conditions, wind chill and wind speed as the most influential factors in determining the duration of an accident.

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