[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118578-en":3,"doc-seo-118578-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118578,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Forecasting Road Incident Duration Using Machine Learning Framework","Traffic congestion caused by nonrecurring incidents such as vehicle crashes and debris is a key challenge for Traffic Management Centers (TMCs). Timely incident clearance is essential for improving safety while reducing traveler delays and emissions. TMCs struggle to predict how long an incident will last until the roadway is clear, making resource deployment difficult. This research proposes an analytical framework and end-to-end machine learning solution that predicts incident duration from information available immediately after an incident report. A model combining classification and regression modules is evaluated using MAE, AUC, and MAPE, showing significant improvement over prior approaches.","Journal of Transportation Technologies, 2025, 15(2), 222-251  \n[https://www.scirp.org/journal/jtts](https://www.scirp.org/journal/jtts)  \nISSN Online: 2160-0481  \nISSN Print: 2160-0473  \nForecasting Road Incident Duration Using Machine Learning Framework  \nSmrithi Ajit1, Varsha R. Mouli2, Skylar Knickerbocker2, Jonathan S. Wood2*  \n1College of Nursing, Michigan State University, East Lansing, MI, USA  \n2Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA, USA  \nHow to cite this paper: Ajit, S., Mouli, V.R., Knickerbocker, S. and Wood, J.S. (2025) Forecasting Road Incident Duration Using Machine Learning Framework. Journal of Transportation Technologies, 15, 222-251.  \n[https://doi.org/10.4236/jtts.2025.152012](https://doi.org/10.4236/jtts.2025.152012)  \nReceived: January 27, 2025  \nAccepted: March 11, 2025  \nPublished: March 14, 2025  \nCopyright © 2025 by author(s) and Scientific Research Publishing Inc.  \nThis work is licensed under the Creative Commons Attribution International License (CC BY 4.0) .  \n[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)  \nOpen Access  \nAbstract  \nTraffic 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 delaysand 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.  \nKeywords  \nTraffic Incident Management, Model Blending, Model Selection, Decision Making, Transportation Forecasting  \n1. Introduction  \nAccording to the Traffic Incident Management Handbook, an incident is defined as a non-recurring event that results in a reduction in the capacity of the roadway  \nDOI: 10.4236/jtts.2025.152012 Mar. 14, 2025 222 Journal of Transportation Technologies  \nor an abnormal increase in demand [1] . These incidents include, but are not limited to, vehicle crashes, disabled vehicles, debris, and spilled cargo. Incidents not only result in traveler delay but also increase the likelihood of secondary crashes and other secondary effects due to increased opportunities for secondary events to occur [2] . Secondary events can lead to increased demand for police, fire, and emergency services, reduced air quality, and other environmental impacts.  \nTotal incident duration is comprised of incident notification time, response time, and clearance time, as illustrated in Figure 1. As shown, the total incident duration is the total time from the start of the incident until the reported time of clearance for the event [3] and includes incident notification, response, and clearance times. The incident notification time is from the start of the incident until the time it is reported. The response time is from the report time until the response unit’s arrival. The clearance time is the time taken to clear the incident after emer","cbCailnsVX7dFNEs","https://ap.wps.com/l/cbCailnsVX7dFNEs","pdf",3335810,1,30,"English","en",105,"# Introduction\n## Incident definition and impacts\n## Components of total incident duration\n## Need for predictive decision support\n# Abstract\n## Problem statement and motivation\n## Proposed analytical and ML framework\n## Evaluation metrics and results","[{\"question\":\"Why is predicting road incident duration important for Traffic Management Centers (TMCs)?\",\"answer\":\"Accurate duration forecasts enable timely mitigation of congestion, earlier warnings to travelers, and reduced risk of secondary crashes through better planning of responses.\"},{\"question\":\"What does total incident duration include?\",\"answer\":\"Total incident duration is the time from incident start to reported clearance time, comprising incident notification time, response time, and clearance time.\"},{\"question\":\"How does the proposed approach predict incident duration?\",\"answer\":\"It develops an end-to-end machine learning solution that uses a combination of classification and regression modules to predict duration based on information available right after an incident report.\"}]","Forecasting Road Incident Duration Using Machine Learning Framework | 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is predicting road incident duration important for Traffic Management Centers (TMCs)?","Question",{"text":75,"@type":76},"Accurate duration forecasts enable timely mitigation of congestion, earlier warnings to travelers, and reduced risk of secondary crashes through better planning of responses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does total incident duration include?",{"text":80,"@type":76},"Total incident duration is the time from incident start to reported clearance time, comprising incident notification time, response time, and clearance time.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach predict incident duration?",{"text":84,"@type":76},"It develops an end-to-end machine learning solution that uses a combination of classification and regression modules to predict duration based on information available right after an incident 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