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Travel Behavior Analysis of Event-Related Urban Rail Transit Passengers during Large Special Events

机译：大型特殊事件中与事件相关的城市轨道交通乘客的出行行为分析

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Large special events always generate additional traffic demands (i.e., event-related passengers), which usually stresses the urban rail transit with a much more severe test than normal conditions. In order to provide better operational measures and guarantee traffic security during the event, it is important to identify the impact and evaluate the influence of the event-related passengers (ERPs). Considering the China import and export fair (Canton Fair) as an example, this paper proposes a data mining method to identify the influenced stations and estimate the event-related passenger flow using data gathered by the automated fare collection (AFC) system of Guangzhou Metro. Meanwhile, based on the analysis of land-use and other attributes of urban rail transit stations, a multinomial logit (MNL) based destination choice model is introduced to account for the ERPs' travel behaviors. The result shows that station attributes (e.g., attraction of the stations, land use type, land use intensity) and some level of service variables (such as travel time, transfer time, and station betweenness calculated by network analysis) play an important role in the destination choice behavior of the ERPs. Thus, the model is capable of accurately describing the ERPs' choice behavior during large special event. The study is helpful to accurately estimate the passengers' destination choice behavior and make reasonable operational plans during large special events for urban rail transit operator.
机译：大型特殊事件总是会产生额外的交通需求（即与事件相关的乘客），这通常会给城市轨道交通带来压力，其测试要比正常情况下更为严峻。为了在活动期间提供更好的操作措施并确保交通安全，重要的是要确定影响并评估与事件相关的乘客（ERP）的影响。以中国进出口商品交易会（广交会）为例，提出一种数据挖掘方法，利用广州地铁自动收费系统（AFC）收集的数据，识别出受影响的车站并估算与事件相关的客流。 。同时，在分析城市轨道交通车站的土地利用和其他属性的基础上，引入了基于多项式logit（MNL）的目的地选择模型来说明ERP的出行行为。结果表明，车站属性（例如车站的吸引力，土地利用类型，土地利用强度）和一定水平的服务变量（例如通过网络分析计算出的旅行时间，中转时间和车站之间的间隔）在其中起着重要作用。 ERP的目的地选择行为。因此，该模型能够准确描述大型特殊事件中ERP的选择行为。该研究有助于准确估计乘客的目的地选择行为，并为城市轨道交通运营商在大型特殊事件中制定合理的运营计划。

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