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Traffic congestion caused by non-recurring incidents such as vehicle crashesand debris is a key issue for Traffic Management Centers (TMCs). Clearingincidents in a timely manner is essential for improving safety and reducingdelays and emissions for the traveling public. However, TMCs and otherresponders face a challenge in predicting the duration of incidents (until theroadway is clear), making decisions of what resources to deploy difficult. Toaddress this problem, this research developed an analytical framework andend-to-end machine-learning solution for predicting incident duration based oninformation available as soon as an incident report is received. Qualitypredictions of incident duration can help TMCs and other responders take aproactive approach in deploying responder services such as tow trucks,maintenance crews or activating alternative routes. The predictions use acombination of classification and regression machine learning modules. Theperformance of the developed solution has been evaluated based on the MeanAbsolute Error (MAE), or deviation from the actual incident duration as well asArea Under the Curve (AUC) and Mean Absolute Percentage Error (MAPE). Theresults showed that the framework significantly improved incident durationprediction compared to methods from previous research.

Machine learning framework for end-to-end implementation of Incident duration prediction