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

• URL: http://arxiv.org/abs/2304.11507v1
• Date: Sun, 23 Apr 2023 00:55:19 GMT
• Title: Machine learning framework for end-to-end implementation of Incident duration prediction
• Authors: Smrithi Ajit, Varsha R Mouli, Skylar Knickerbocker, Jonathan S. Wood
• Abstract summary: This research developed an analytical framework and end-to-end machine-learning solution for predicting incident duration based on information available as soon as an incident report is received. The results showed that the framework significantly improved incident duration prediction compared to methods from previous research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic congestion caused by non-recurring incidents such as vehicle crashes and debris is a key issue for Traffic Management Centers (TMCs). Clearing incidents in a timely manner is essential for improving safety and reducing 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 of what resources to deploy difficult. To address this problem, this research developed an analytical framework and end-to-end machine-learning solution for predicting incident duration based on 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, 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 incident duration prediction compared to methods from previous research.

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