相互依赖公交轨道交通网络弹性评估框架:结构、关键组件和耦合机制

IF 12.5 Q1 TRANSPORTATION
Bing Liu , Xiaoyue Liu , Yang Yang , Xi Chen , Xiaolei Ma
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引用次数: 0

摘要

了解多式联运公共交通网络的相互依存性对于确保交通系统的弹性和稳健性至关重要。然而,以前的研究主要集中在评估单模PTN的脆弱性和特征上,忽略了多模式PTN中异质干扰和旅行行为变化的影响。因此,本研究引入了一种新的弹性评估框架,全面分析了公交-轨道交通网络的耦合机制、结构和功能特征。在该框架中,通过考虑乘客在各种干扰下的出行行为,提出了一种网络性能指标。此外,通过模拟在每个站或子网络处发生的各种干扰,使用k均值算法和弹性度量对站和子网络进行分类。通过对中国北京BRTN的案例研究,验证了所提出的框架。结果表明,在相互依存的BRTN中,轨道交通网络在维护网络功能和抵御外部干扰方面发挥着至关重要的作用。此外,在基础设施组件中断和功能中断的情况下,RTN和公交网络(BTN)之间的耦合相互作用表现出不同的特征。这些发现为PTN的应急管理以及理解BTN和RTN之间的耦合关系提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resilience assessment framework toward interdependent bus–rail transit network: Structure, critical components, and coupling mechanism

Understanding the interdependent nature of multimodal public transit networks (PTNs) is vital for ensuring the resilience and robustness of transportation systems. However, previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs, neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal PTNs. Therefore, this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism, structural and functional characteristics of bus–rail transit networks (BRTNs). In this framework, a network performance metric is proposed by considering the passengers’ travel behaviors under various disturbances. Additionally, stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or subnetwork. The proposed framework is validated via a case study of a BRTN in Beijing, China. Results indicate that the rail transit network (RTN) plays a crucial role in maintaining network function and resisting external disturbances in the interdependent BRTN. Furthermore, the coupling interactions between the RTN and bus transit network (BTN) exhibit distinct characteristics under infrastructure component disruption and functional disruption. These findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

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CiteScore
15.20
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