Timetabling for a congested urban rail transit network based on mixed logic dynamic model

被引:3
|
作者
Hao, Sijia [1 ]
Song, Rui [1 ]
He, Shiwei [1 ]
机构
[1] Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Timetabling; congested urban rail transit network; internal passenger flow; mixed logic dynamic model; network state evolution; simulation-based optimization; PASSENGER FLOW-CONTROL; TRAIN REGULATION; OPTIMIZATION; GENERATION; ALGORITHM; TIME; PERFORMANCE; DEMAND;
D O I
10.1080/21680566.2021.1965925
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
This paper focuses on the timetabling problem for a congested urban rail transit network. During peak hours, the situation of premature full load of trains and passenger stranded on platforms is serious. To minimize the total passenger's travelling time and optimize resource allocation from a planning and management perspective, we develop an optimization model considering dwelling time as one of the decision variables to control the number of boarding passengers. In the proposed model, the network internal passenger flow is quantitatively characterized by three types of flows according to its origin and destination under the distributed structure. And the mixed logic dynamic model describes the operation of network, especially the interaction between lines. Furthermore, the number of time-varying transfer passengers is calculated which has never been studied before. At last, we adopt a simulation-based approach to solve the model and prove the effectiveness through two cases.
引用
收藏
页码:139 / 158
页数:20
相关论文
共 50 条
  • [31] Integrated rolling stock deadhead routing and timetabling in urban rail transit lines
    Wang, Dian
    D'Ariano, Andrea
    Zhao, Jun
    Zhong, Qingwei
    Peng, Qiyuan
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2022, 298 (02) : 526 - 559
  • [32] Morning commute in congested urban rail transit system: a macroscopic model for equilibrium distribution of passenger arrivals
    Zhang, Jiahua
    Wada, Kentaro
    Oguchi, Takashi
    [J]. TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 2023, 11 (01)
  • [33] Cell transmission model of dynamic assignment for urban rail transit networks
    Xu, Guangming
    Zhao, Shuo
    Shi, Feng
    Zhang, Feilian
    [J]. PLOS ONE, 2017, 12 (11):
  • [34] Quasi-dynamic assignment for congested transit network
    Ren, HL
    Gao, ZY
    Lam, WHK
    [J]. PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING, VOLS I AND II, 2003, : 567 - 571
  • [35] Construction Rules of Urban Rail Transit Network Based on Complex Network Eigenvalue
    Feng, Liping
    Hu, Xuexue
    [J]. PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON TRANSPORTATION ENGINEERING (ICTE 2019), 2019, : 540 - 548
  • [36] Passenger route choice model and algorithm in the urban rail transit network
    Qiao, Ke
    Zhao, Peng
    Qin, Zhi-Peng
    [J]. JOURNAL OF INDUSTRIAL ENGINEERING AND MANAGEMENT-JIEM, 2013, 6 (01): : 113 - 123
  • [37] Quantitative Modeling and Comprehensive Evaluation of Urban Rail Transit Network Dynamic Accessibility
    Li, Wei
    Luo, Qin
    Zhou, Jingnan
    Zhang, Xiongfei
    [J]. 2018 3RD IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (ICITE), 2018, : 95 - 99
  • [38] Demand-driven flexible-periodicity train timetabling model and algorithm for a rail transit network
    Yin, Yonghao
    Li, Dewei
    Han, Zhenyu
    Zhang, Songliang
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2024, 187
  • [39] Cascading Failure Analysis of Urban Rail Transit Network Based on Coupled Map Lattice Model
    Feng, Shumin
    Wang, Sa
    Zhao, Hu
    [J]. CICTP 2020: ADVANCED TRANSPORTATION TECHNOLOGIES AND DEVELOPMENT-ENHANCING CONNECTIONS, 2020, : 2074 - 2084
  • [40] Urban Rail Transit System Network Reliability Analysis Based on a Coupled Map Lattice Model
    Wu, Shaojie
    Zhu, Yan
    Li, Ning
    Wang, Yizeng
    Wang, Xingju
    Sun, Daniel Jian
    [J]. JOURNAL OF ADVANCED TRANSPORTATION, 2021, 2021