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首页> 外文期刊>Transportation >Modelling the resilience of rail passenger transport networks affected by large-scale disruptive events: the case of HSR (high speed rail)
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Modelling the resilience of rail passenger transport networks affected by large-scale disruptive events: the case of HSR (high speed rail)

机译：建模受大规模破坏性事件影响的铁路客运网络的复原力：高铁（HSR）案例

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This paper deals with modelling the dynamic resilience of rail passenger transport networks affected by large-scale disruptive events whose impacts deteriorate the networks’ planned infrastructural, operational, economic, and social-economic performances represented by the selected indicators. The indicators of infrastructural performances refer to the physical and operational conditions of the networks’ lines and stations, and supportive facilities and equipment. Those of the operational performances include transport services scheduled along particular routes, their seating capacity, and corresponding transport work/capacity. The indicators of economic performances include the costs of cancelled and long-delayed transport services imposed on the main actors/stakeholder involved—the rail operator(s) and users/passengers. The indicators of social-economic performances reflect the compromised accessibility and consequent prevention of the user/passenger trips and their contribution to the local/regionalational Gross Domestic Product. Modeling resulted in developing a methodology including two sets of analytical models for: (1) assessing the dynamic resilience of a given rail network, i.e., before, during, and after the impacts of disruptive event(s); and (2) estimation of the indicators of particular performances as the figures-of-merit for assessing the network’s resilience under the given conditions. As such, the methodology could be used for estimating the resilience of different topologies of rail passenger networks affected by past, current, and future disruptive events, the latest according to the “what-if” scenario approach and after introducing the appropriate assumptions. The methodology has been applied to a past case—the Japanese Shinkansen HSR network affected by a large-scale disruptive event—the Great East Japan Earthquake on 11 March 2011.
机译：本文研究了受大规模破坏性事件影响的铁路客运网络动态弹性的建模，这些事件破坏了以所选指标表示的网络计划的基础设施，运营，经济和社会经济绩效。基础设施性能指标是指网络的线路和站点以及辅助设施和设备的物理和运行状况。运营绩效包括沿特定路线安排的运输服务，座位容量以及相应的运输工作/能力。经济绩效指标包括对主要参与者/利益相关者（铁路运营商和用户/乘客）施加的取消和延误的运输服务成本。社会经济绩效指标反映出无障碍通行性和使用者/旅客出行的后果及其对当地/区域/国家国内生产总值的贡献。通过建模，开发了一套包括两套分析模型的方法，用于：（1）评估给定铁路网络的动态弹性，即在破坏性事件的影响之前，之中和之后； （2）评估特定性能的指标，作为评估给定条件下网络弹性的品质因数。因此，该方法可用于估计受过去，当前和未来破坏性事件影响的铁路客运网络的不同拓扑的复原力，这是根据“假设”场景方法得出的最新结果，并引入了适当的假设。该方法已应用于过去的案例-受大规模破坏性事件影响的日本新干线高铁网络-2011年3月11日发生的东日本大地震。

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