Rescheduling models for railway traffic management in large-scale networks

被引:58
|
作者
Kecman P. [1 ]
Corman F. [2 ]
D'Ariano A. [3 ]
Goverde R.M.P. [1 ]
机构
[1] Department of Transport and Planning, Delft University of Technology, Delft
[2] Center for Industrial Management, Catholic University Leuven, Leuven
[3] Dipartimento di Informatica e Automazione, Università degli Studi Roma Tre, Rome
关键词
Alternative graph; Delay propagation; Macroscopic modeling; Railway traffic management; Timed event graph;
D O I
10.1007/s12469-013-0063-y
中图分类号
学科分类号
摘要
In the last decades of railway operations research, microscopic models have been intensively studied to support traffic operators in managing their dispatching areas. However, those models result in long computation times for large and highly utilized networks. The problem of controlling country-wide traffic is still open since the coordination of local areas is hard to tackle in short time and there are multiple interdependencies between trains across the whole network. This work is dedicated to the development of new macroscopic models that are able to incorporate traffic management decisions. Objective of this paper is to investigate how different levels of detail and number of operational constraints may affect the applicability of models for network-wide rescheduling in terms of quality of solutions and computation time. We present four different macroscopic models and test them on the Dutch national timetable. The macroscopic models are compared with a state-of-the-art microscopic model. Trade-off between computation time and solution quality is discussed on various disturbed traffic conditions. © 2013 Springer-Verlag Berlin Heidelberg.
引用
收藏
页码:95 / 123
页数:28
相关论文
共 50 条
  • [41] The impacts of Internet traffic variability on modelling for large-scale broadband networks
    Swift, Douglas K.
    Dagli, Cihan H.
    PROCEEDINGS OF THE SIXTH IASTED INTERNATIONAL CONFERENCE ON COMMUNICATIONS, INTERNET, AND INFORMATION TECHNOLOGY, 2007, : 159 - 165
  • [42] Multimodal fusion for large-scale traffic prediction with heterogeneous retentive networks
    Yan, Yimo
    Cui, Songyi
    Liu, Jiahui
    Zhao, Yaping
    Zhou, Bodong
    Kuo, Yong-Hong
    Information Fusion, 2025, 114
  • [43] Large-scale Network Traffic Prediction With LSTM and Temporal Convolutional Networks
    Bi, Jing
    Yuan, Haitao
    Xu, Kangyuan
    Ma, Haisen
    Zhou, Mengchu
    2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022), 2022, : 3865 - 3870
  • [44] Performance analysis of large-scale IP networks considering TCP traffic
    Hisamatsu, Hiroyuki
    Hasegawa, Go
    Murata, Masayuki
    IEICE TRANSACTIONS ON COMMUNICATIONS, 2007, E90B (10) : 2845 - 2853
  • [45] A fast network partition method for large-scale urban traffic networks
    Zhao ZHOU
    Shu LIN
    Yugeng XI
    Control Theory and Technology, 2013, 11 (03) : 359 - 366
  • [46] Multidimensional Data Mining of Traffic Anomalies on Large-Scale Road Networks
    Gonzalez, Hector
    Han, Jiawei
    Ouyang, Yanfeng
    Seith, Sebastian
    TRANSPORTATION RESEARCH RECORD, 2011, (2215) : 75 - 84
  • [47] Subnetwork estimation for spatial autoregressive models in large-scale networks*
    Li, Xuetong
    Wang, Feifei
    Lan, Wei
    Wang, Hansheng
    ELECTRONIC JOURNAL OF STATISTICS, 2023, 17 (01): : 1768 - 1805
  • [48] Learn to decompose multiobjective optimization models for large-scale networks
    Aslani, Babak
    Mohebbi, Shima
    INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, 2024, 31 (02) : 949 - 978
  • [49] Anomaly Detection in Large-Scale Networks With Latent Space Models
    Lee, Wesley
    McCormick, Tyler H.
    Neil, Joshua
    Sodja, Cole
    Cui, Yanran
    TECHNOMETRICS, 2022, 64 (02) : 241 - 252
  • [50] An Integer Optimization Approach to Large-Scale Air Traffic Flow Management
    Bertsimas, Dimitris
    Lulli, Guglielmo
    Odoni, Amedeo
    OPERATIONS RESEARCH, 2011, 59 (01) : 211 - 227