A discrete-event model of the train traffic on a linear metro line

被引:6
作者
Farhi, Nadir [1 ]
机构
[1] Univ Gustave Eiffel, IFSTTAR, COSYS GRETTIA, F-77454 Marne La Vallee, France
关键词
Railway traffic modeling; Traffic control; Physics of traffic; Dynamic systems; Max-plus algebra;
D O I
10.1016/j.apm.2021.03.012
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A B S T R A C T We present in this article a mathematical model for the traffic on a linear metro line (a metro line without junction), taking into account the train dynamics and the passenger travel demand. The train dynamics are modeled as a discrete event dynamic system, written linearly in the Max-plus algebra. The model permits an analytic derivation of phase diagrams for the train dynamics depending on the passenger travel demand. By this, the physics of traffic on linear metro lines is wholly understood and interpreted. We introduce into the model the passenger capacity of trains, and derive analytically an indicator for the passenger comfort inside the trains, as a function of the number of trains and of the level of the passenger demand. By this model, metro operators can adjust the train dwell times at platforms depending on the level of the passenger demand. They can also optimize the train frequency with respect to the number of trains, and depending on the level of the passenger demand. Furthermore, the level of the passenger comfort inside the trains can be taken as the result of a compromise between the passengers' satisfaction, in one side, and the number of trains as well as the passenger capacity of trains, in terms of the economic criteria of the operator, in the other side. (c) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页码:523 / 544
页数:22
相关论文
共 22 条
[1]  
Baccelli F., 1992, SYNCHRONIZATION LINE
[2]   Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty [J].
Cacchiani, Valentina ;
Qi, Jianguo ;
Yang, Lixing .
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2020, 136 :1-29
[3]   An overview of recovery models and algorithms for real-time railway rescheduling [J].
Cacchiani, Valentina ;
Huisman, Dennis ;
Kidd, Martin ;
Kroon, Leo ;
Toth, Paolo ;
Veelenturf, Lucas ;
Wagenaar, Joris .
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2014, 63 :15-37
[4]  
Cochet-Terrasson J., 1998, P IFAC C SYST STRUCT, P699
[5]   Macroscopic fundamental diagrams for train operations - are we there yet? [J].
Corman, Francesco ;
Henken, Jonas ;
Keyvan-Ekbatani, Mehdi .
MT-ITS 2019: 2019 6TH INTERNATIONAL CONFERENCE ON MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS (MT-ITS), 2019,
[6]   A Review of Online Dynamic Models and Algorithms for Railway Traffic Management [J].
Corman, Francesco ;
Meng, Lingyun .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 16 (03) :1274-1284
[8]   Adaptive railway traffic control using approximate dynamic programming [J].
Ghasempour, Taha ;
Heydecker, Benjamin .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2020, 113 :91-107
[9]   Train timetabling with dynamic and random passenger demand: A stochastic optimization method [J].
Gong, Congcong ;
Shi, Jungang ;
Wang, Yanhui ;
Zhou, Housheng ;
Yang, Lixing ;
Chen, Dewang ;
Pan, Hanchuan .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 123
[10]  
Goverde R.M. P., 2005, Punctuality of railway operations and timetable stability analysis