(Peer-Reviewed) Coupling analysis of passenger and train flows for a large-scale urban rail transit system
Ping ZHANG 张萍 ¹, Xin YANG 杨欣 ¹, Jianjun WU 吴建军 ¹, Huijun SUN 孙会君 ¹, Yun WEI 魏运 ², Ziyou GAO 高自友 ¹
¹ State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
中国 北京 北京交通大学 轨道交通控制与安全国家重点实验室
² Beijing Mass Transit Railway Operation Corp. Ltd., Beijing 100044, China
中国 北京 北京市轨道交通运营管理有限公司
Abstract
Coupling analysis of passenger and train flows is an important approach in evaluating and optimizing the operation efficiency of large-scale urban rail transit (URT) systems. This study proposes a passenger–train interaction simulation approach to determine the coupling relationship between passenger and train flows.
On the bases of time-varying origin–destination demand, train timetable, and network topology, the proposed approach can restore passenger behaviors in URT systems. Upstream priority, queuing process with first-in-first-serve principle, and capacity constraints are considered in the proposed simulation mechanism.
This approach can also obtain each passenger’s complete travel chain, which can be used to analyze (including but not limited to) various indicators discussed in this research to effectively support train schedule optimization and capacity evaluation for urban rail managers. Lastly, the proposed model and its potential application are demonstrated via numerical experiments using real-world data from the Beijing URT system (i.e., rail network with the world’s highest passenger ridership).
Flicker minimization in power-saving displays enabled by measurement of difference in flexoelectric coefficients and displacement-current in positive dielectric anisotropy liquid crystals
Junho Jung, HaYoung Jung, GyuRi Choi, HanByeol Park, Sun-Mi Park, Ki-Sun Kwon, Heui-Seok Jin, Dong-Jin Lee, Hoon Jeong, JeongKi Park, Byeong Koo Kim, Seung Hee Lee, MinSu Kim
Opto-Electronic Advances
2025-09-25
Dual-frequency angular-multiplexed fringe projection profilometry with deep learning: breaking hardware limits for ultra-high-speed 3D imaging
Wenwu Chen, Yifan Liu, Shijie Feng, Wei Yin, Jiaming Qian, Yixuan Li, Hang Zhang, Maciej Trusiak, Malgorzata Kujawinska, Qian Chen, Chao Zuo
Opto-Electronic Advances
2025-09-25