Modeling disruption durations of subway service via random survival forests: The case of Shanghai

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Xinyuan Wang, Jian Li, Rongjie Yu
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引用次数: 4

Abstract

Abstract Subways are the backbone of many urban transportation systems in large cities around the world. However, disruptions of subway services, such as power-supply failure or signal failures, can cause severe travel delays due to the large carrying capacities of subway trains. Accurate predictions of the disruption durations of subway services are essential for emergency response. To predict the disruption durations of subway services, previous studies primarily used parametric models (e.g., the accelerated failure time (AFT) model), and less attention has been given to machine-learning models with high potential prediction ability and fewer parameter restrictions. This paper proposes a model to predict and explore factors that affect the disruption durations of subway services in Shanghai using machine-learning models. The longitudinal data released by the subway operator from 2012 to 2021 were collected and analyzed in this study. A random survival forest (RSF) was used to describe the disruption durations of the subway service, and influential factors, such as incident reason, incident occurrence time, and line-related variables were considered in the model. Results show that the RSF model (C-Index = 0.672) achieved better prediction accuracy than the traditional AFT model (the best C-Index = 0.609) based on the collected data in Shanghai. In addition, results indicate that incident reason, disruption location, and the time of disruption factors can significantly affect subway service disruption durations. The proposed model can be used as a tool to predict the disruption durations of subway service for better disruption management of the subway system.
基于随机生存森林的地铁服务中断时间建模:以上海为例
地铁是世界上许多大城市交通系统的支柱。然而,地铁服务的中断,如电源故障或信号故障,可能会造成严重的旅行延误,因为地铁列车的运载能力很大。准确预测地铁服务中断时间对应急响应至关重要。为了预测地铁服务中断时间,以往的研究主要使用参数模型(如加速故障时间(AFT)模型),而对潜在预测能力高、参数限制较少的机器学习模型的关注较少。本文提出了一个模型,利用机器学习模型来预测和探索影响上海地铁服务中断时间的因素。本研究收集了2012 - 2021年地铁运营商发布的纵向数据并进行了分析。该模型采用随机生存森林(RSF)来描述地铁服务中断时间,并考虑了事件原因、事件发生时间和线路相关变量等影响因素。结果表明,基于上海实测数据,RSF模型(C-Index = 0.672)的预测精度优于传统AFT模型(C-Index = 0.609)。此外,研究结果表明,事件原因、中断地点和中断时间对地铁服务中断时间有显著影响。该模型可以作为预测地铁服务中断时间的工具,以更好地进行地铁系统的中断管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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