Salehi Amirreza, Babaei Ardavan, Khedmati Majid
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
PLoS One. 2025 Jan 2;20(1):e0316289. doi: 10.1371/journal.pone.0316289. eCollection 2025.
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.
预测事件持续时间并了解事件类型对于交通管理中的资源优化和干扰最小化至关重要。精确的预测能够有效部署应急响应团队并进行战略性交通改道，从而减少拥堵并提高安全性。此外，深入了解事件类型有助于实施预防措施并制定策略以减轻其对道路网络的影响。在本文中，我们提出了一个全面的框架来准确预测事件持续时间，特别强调街道条件和位置作为主要事件触发因素的关键作用。为了证明我们框架的有效性，我们使用来自旧金山的数据集进行了深入的案例研究。我们引入了一个从风险优先数（RPN）概念衍生而来的名为“风险”的新特征，突出了事件位置在事件发生和预测中的重要性。此外，我们通过模糊聚类方法提出了一种细化的事件分类，划定了一个独特的策略来识别在不同场景下需要进一步建模和测试的边界聚类。每个聚类都经过多准则决策（MCDM）过程，以更深入地了解它们的差异并提供有价值的管理见解。最后，我们使用传统机器学习（ML）和深度学习（DL）模型来执行分类和回归任务。具体而言，利用本研究中概述的场景来预测位于边界聚类中的事件。通过使用表现最佳的预测模型对特征重要性进行严格分析，我们确定“风险”因素是事件持续时间的关键决定因素。此外，距离、湿度和时间等变量显示出显著影响，进一步增强了所提出模型的预测能力。
