TITLE:
Forecasting Road Incident Duration Using Machine Learning Framework
AUTHORS:
Smrithi Ajit, Varsha R. Mouli, Skylar Knickerbocker, Jonathan S. Wood
KEYWORDS:
Traffic Incident Management, Model Blending, Model Selection, Decision Making, Transportation Forecasting
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.15 No.2,
March
14,
2025
ABSTRACT: Traffic congestion caused by nonrecurring incidents such as vehicle crashes and debris is a key issue for Traffic Management Centers (TMCs). Clearing incidents in a timely manner is essential to improve safety and reduce delays and emissions for the traveling public. However, TMCs and other responders face a challenge in predicting the duration of incidents (until the roadway is clear), making decisions about what resources to deploy is difficult. To address this problem, this research developed an analytical framework and end-to-end machine learning solution to predict the duration of the incident based on the information available as soon as an incident report is received. Quality predictions of incident duration can help TMCs and other responders take a proactive approach in deploying responder services such as tow trucks, and maintenance crews, or activating alternative routes. The predictions use a combination of classification and regression machine learning modules. The performance of the developed solution has been evaluated based on the Mean Absolute Error (MAE), or deviation from the actual incident duration as well as Area Under the Curve (AUC) and Mean Absolute Percentage Error (MAPE). The results showed that the framework significantly improved the prediction of incident duration compared to previous research methods.