测量和预测降雨事件下地铁的抗灾能力:环境视角

IF 7.3 1区 工程技术 Q1 ENVIRONMENTAL STUDIES
Wei Gao , Yiyang Lu , Naihui Wang , Guozhu Cheng , Zhenyang Qiu , Xiaowei Hu
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引用次数: 0

摘要

降雨事件经常干扰地铁系统,严重影响运营效率和服务质量。由于地铁站的施工环境各不相同,因此测量和预测地铁系统的恢复能力具有挑战性。我们开发了一种基于概率建模技术的方法来测量地铁系统和车站的恢复能力。随机森林用于从环境角度分析弹性模式的异质性。基于小波分解和时空网络,我们设计了一个考虑环境因素的集合神经网络建模框架,以预测系统和车站的复原力。根据对中国哈尔滨数据集的分析,当降雨量低于 60 毫米时,降雨强度每增加 10 毫米,地铁系统的恢复能力就会下降 1/6。44.6%的低弹性车站靠近服务等级为 III 和 IV 级的道路。所提出的预测模型优于最先进的模型,预测准确率达 96.82%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measurement and prediction of subway resilience under rainfall events: An environment perspective
Rainfall events frequently disrupt the subway system, significantly impacting operational efficiency and service quality. It is challenging to measure and predict subway system resilience due to the different construction environments of subway stations. We develop an approach based on probabilistic modeling techniques to measure subway system and station resilience. Random forest is used to analyze the heterogeneity of resilience patterns from an environmental perspective. Based on wavelet decomposition and spatial–temporal networks, we design an ensemble neural network modeling framework considering environmental factors to predict system and station resilience. According to an analysis of a dataset from Harbin, China, subway system resilience decreases by 1/6 for every 10 mm increase in rainfall intensity when the rainfall is under 60 mm. 44.6 % of low-resilience stations are near roads at the Level of Service III and IV. The proposed prediction model outperforms the state-of-the-art models with a prediction accuracy of 96.82 %.
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来源期刊
CiteScore
14.40
自引率
9.20%
发文量
314
审稿时长
39 days
期刊介绍: Transportation Research Part D: Transport and Environment focuses on original research exploring the environmental impacts of transportation, policy responses to these impacts, and their implications for transportation system design, planning, and management. The journal comprehensively covers the interaction between transportation and the environment, ranging from local effects on specific geographical areas to global implications such as natural resource depletion and atmospheric pollution. We welcome research papers across all transportation modes, including maritime, air, and land transportation, assessing their environmental impacts broadly. Papers addressing both mobile aspects and transportation infrastructure are considered. The journal prioritizes empirical findings and policy responses of regulatory, planning, technical, or fiscal nature. Articles are policy-driven, accessible, and applicable to readers from diverse disciplines, emphasizing relevance and practicality. We encourage interdisciplinary submissions and welcome contributions from economically developing and advanced countries alike, reflecting our international orientation.
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