Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study
Yükleniyor...
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Public Library of Science
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Predicting 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.
Açıklama
Anahtar Kelimeler
Kaynak
PLoS ONE
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
20
Sayı
1
Künye
Salehi, 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.
