The Subway Passenger Flow Macroscopic State Analysis

Recently, the ridership explosion brings great challenges to the service of subway companies such as crowdedness in trains and the insufficient capacity of subway facilities. Thus, understanding, describing, and controlling the subway passenger flow state is necessary to mitigate estimated or forecasted traffic congestion. In this context, we incorporated the passenger volume of the station which includes inbound, outbound and transfer process and a load of section into the exploration of system element states. Thus, the network can be examined in the context of operation, which is the ultimate purpose of the existence of a subway network. Utilization rate and load factor were proposed to describe the state of station and section. Based on the basic states of elements, system passenger flow macroscopic states which can describe the subway system operation condition from a global perspective are presented with the entropy. Finally, Beijing subway system is implemented to validate the accuracy and superiority of this method.

[1]  Yanhui Wang,et al.  The modeling of attraction characteristics regarding passenger flow in urban rail transit network based on field theory , 2017, PloS one.

[2]  Feng Chen,et al.  Passenger flow analysis of Beijing urban rail transit network using fractal approach , 2018 .

[3]  Shuai Su,et al.  Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model , 2016 .

[4]  Yang Li,et al.  Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks ☆ , 2017 .

[5]  Baohua Mao,et al.  Network structure of subway passenger flows , 2016 .

[6]  Li Zhu,et al.  The Evolution Analysis of Guangzhou Subway Network by Complex Network Theory , 2016 .

[7]  Wei Li,et al.  A dynamic simulation model of passenger flow distribution on schedule-based rail transit networks with train delays , 2016 .

[8]  Yang Yang,et al.  Modeling Passenger Flow Distribution Based on Disaggregate Model for Urban Rail Transit , 2014 .

[9]  G. Dong,et al.  Journey to the east: Diverse routes and variable flowering times for wheat and barley en route to prehistoric China , 2017, PloS one.

[10]  Yongxue Liu,et al.  Robustness assessment of urban rail transit based on complex network theory: a case study of the Beijing Subway , 2015 .

[11]  Haiying Li,et al.  Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study , 2016 .

[12]  Zhong Wu,et al.  Modeling Passenger Flow Distribution Based on Travel Time of Urban Rail Transit , 2011 .