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基于网络瓶颈控制的轨道交通车站客流疏导研究
引用本文:王 琨,孙立山,罗 薇,王顺超,张亚军. 基于网络瓶颈控制的轨道交通车站客流疏导研究[J]. 都市快轨交通, 2019, 32(3): 84-90
作者姓名:王 琨  孙立山  罗 薇  王顺超  张亚军
作者单位:北京市交通工程重点实验室(北京工业大学),北京,100124;北京市交通工程重点实验室(北京工业大学),北京,100124;北京市交通工程重点实验室(北京工业大学),北京,100124;北京市交通工程重点实验室(北京工业大学),北京,100124;北京市交通工程重点实验室(北京工业大学),北京,100124
基金项目:北京市科技计划项目资助(D161100005616001);北京市自然科学基金重点项目资助(4181002)
摘    要:轨道交通客流在特定时空区间的强聚集效应导致车站内部设施的服务能力与客流量不匹配,极易造成乘客拥堵、滞留等问题。通过对轨道交通车站客流流线大范围调研,确定服务设施实际通行能力,识别车站瓶颈区域。进而利用多点联动的瓶颈控制方法,构建网络瓶颈客流疏导模型,协调流线网络结构中设施通行能力的匹配度,计算瓶颈点上下游客流调节量。以北京东单地铁换乘站为例进行流线瓶颈优化,制定不同客流强度下瓶颈优化方案,验证模型的有效性和实用性。模型可协助车站管理人员明确瓶颈控制区域的范围,并制定详细的限流泄流乘客量分配,为高密度客流组织提供理论依据与数据支撑。

关 键 词:城市轨道交通  车站设施  客流流线  瓶颈控制  量化分析

Passenger Flow Distribution in Railway Stations Based on Network Bottleneck Control Model
WANG Kun,SUN Lishan,LUO Wei,WANG Shunchao,ZHANG Yajun. Passenger Flow Distribution in Railway Stations Based on Network Bottleneck Control Model[J]. Urban Rapid Rail Transit, 2019, 32(3): 84-90
Authors:WANG Kun  SUN Lishan  LUO Wei  WANG Shunchao  ZHANG Yajun
Affiliation:Beijing Key Laboratory of Traffic Engineering (Beijing University of Technology), Beijing 100124
Abstract:The service capacity of subway station facilities does not accommodate huge passenger demands due to the strong cluster effect of subway passenger flow in a particular time and space. System service quality declines rapidly, thereby leading to the congestion and detention of passengers in the station during peak periods. In this paper, large-scale investigation of subway station streamline and facilities are carried out and the actual capacity of service facilities are measured. The bottleneck areas of the rail transit station are then classified by passenger streamline. Thus, a network bottleneck passenger flow distribution model is built using multipoint interlink gating control method. The regulation of passenger volume in the bottleneck area is then calculated by analyzing the matching degree of facility capacity in the streamline network structure. A case study of Dongdan station in Beijing is considered where the model is implemented to control streamline bottleneck. In addition, the bottleneck optimization scheme is calculated for different passenger flow intensities. The validity and practicality of model are verified and thus provide the theoretical basis and data support for high-density passenger flow organization. The model can assist station managers to determine the bottleneck control area and to develop detailed passenger flow assignment.
Keywords:urban rail transit   station facility   passenger flow streamline   bottleneck area control   quantitative analysis
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