Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study

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dc.authorscopusidArdavan Babaei / 57193898673
dc.authorwosidArdavan Babaei / JLG-3040-2023
dc.contributor.authorSalehi, Amirreza
dc.contributor.authorBabaei, Ardavan
dc.contributor.authorKhedmati, Majid
dc.date.accessioned2025-04-18T10:31:29Z
dc.date.available2025-04-18T10:31:29Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractPredicting incident duration and understanding incident types are essential in traffic management for resource optimization and disruption minimization. Precise predictions enable the efficient deployment of response teams and strategic traffic rerouting, leading to reduced congestion and enhanced safety. Furthermore, an in-depth understanding of incident types helps in implementing preventive measures and formulating strategies to alleviate their influence on road networks. In this paper, we present a comprehensive framework for accurately predicting incident duration, with a particular emphasis on the critical role of street conditions and locations as major incident triggers. To demonstrate the effectiveness of our framework, we performed an in-depth case study using a dataset from San Francisco. We introduce a novel feature called "Risk" derived from the Risk Priority Number (RPN) concept, highlighting the significance of the incident location in both incident occurrence and prediction. Additionally, we propose a refined incident categorization through fuzzy clustering methods, delineating a unique policy for identifying boundary clusters that necessitate further modeling and testing under varying scenarios. Each cluster undergoes a Multiple Criteria Decision-Making (MCDM) process to gain deeper insights into their distinctions and provide valuable managerial insights. Finally, we employ both traditional Machine Learning (ML) and Deep Learning (DL) models to perform classification and regression tasks. Specifically, incidents residing in boundary clusters are predicted utilizing the scenarios outlined in this study. Through a rigorous analysis of feature importance using top-performing predictive models, we identify the "Risk" factor as a critical determinant of incident duration. Moreover, variables such as distance, humidity, and hour demonstrate significant influence, further enhancing the predictive power of the proposed model. © 2025 Salehi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.identifier.citationSalehi, A., Babaei, A., & Khedmati, M. (2025). Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study. PloS one, 20(1), e0316289.
dc.identifier.doi10.1371/journal.pone.0316289
dc.identifier.issn19326203
dc.identifier.issue1
dc.identifier.pmid39746103
dc.identifier.scopus2-s2.0-85214098966
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0316289
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7101
dc.identifier.volume20
dc.identifier.wosWOS:001389132800070
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.institutionauthorBabaei, Ardavan
dc.language.isoen
dc.publisherPublic Library of Science
dc.relation.ispartofPLoS ONE
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleIncident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study
dc.typeArticle

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