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基于场论的城市轨道交通网络客流吸引特性建模

The modeling of attraction characteristics regarding passenger flow in urban rail transit network based on field theory.

作者信息

Li Man, Wang Yanhui, Jia Limin

机构信息

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China.

School of traffic and transportation, Beijing Jiaotong University, Beijing, China.

出版信息

PLoS One. 2017 Sep 1;12(9):e0184131. doi: 10.1371/journal.pone.0184131. eCollection 2017.

DOI:10.1371/journal.pone.0184131
PMID:28863175
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5581165/
Abstract

Aimed at the complicated problems of attraction characteristics regarding passenger flow in urban rail transit network, the concept of the gravity field of passenger flow is proposed in this paper. We establish the computation methods of field strength and potential energy to reveal the potential attraction relationship among stations from the perspective of the collection and distribution of passenger flow and the topology of network. As for the computation methods of field strength, an optimum path concept is proposed to define betweenness centrality parameter. Regarding the computation of potential energy, Compound Simpson's Rule Formula is applied to get a solution to the function. Taking No. 10 Beijing Subway as a practical example, an analysis of simulation and verification is conducted, and the results shows in the following ways. Firstly, the bigger field strength value between two stations is, the stronger passenger flow attraction is, and the greater probability of the formation of the largest passenger flow of section is. Secondly, there is the greatest passenger flow volume and circulation capacity between two zones of high potential energy.

摘要

针对城市轨道交通网络中客流吸引特性的复杂问题,本文提出了客流重力场的概念。我们建立了场强和势能的计算方法,从客流的集散和网络拓扑的角度揭示车站之间潜在的吸引关系。对于场强的计算方法,提出了最优路径概念来定义介数中心性参数。关于势能的计算,应用复合辛普森法则公式求解函数。以北京地铁10号线为例进行了仿真分析与验证,结果表明:一是两站之间场强值越大,客流吸引力越强,断面最大客流量形成的概率越大;二是高势能的两个区域之间客流量和周转能力最大。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b6b/5581165/5a3cd55db475/pone.0184131.g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b6b/5581165/dcf35f39b5e1/pone.0184131.g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b6b/5581165/5a3cd55db475/pone.0184131.g008.jpg

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