Dual graph representation of transport networks

被引:0
|
作者
Anez, J. [1 ]
De La Barra, T. [1 ]
Perez, B. [1 ]
机构
[1] Modelistica, Caracas, Venezuela
关键词
Computer simulation - Errors - Graph theory - Highway traffic control - Mathematical models - Transportation routes;
D O I
暂无
中图分类号
学科分类号
摘要
The dual graph technique has been developed to represent transport networks. The method is intended to simplify the coding of complex transport networks to a considerable degree, particularly when there are turning restrictions, or when multiple transfers between modes or transit lines must be taken into account. This article briefly describes this technique.
引用
收藏
页码:209 / 216
相关论文
共 50 条
  • [41] Multiplex Graph Representation Learning Via Dual Correlation Reduction
    Mo, Yujie
    Chen, Yuhuan
    Lei, Yajie
    Peng, Liang
    Shi, Xiaoshuang
    Yuan, Changan
    Zhu, Xiaofeng
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 35 (12) : 12814 - 12827
  • [42] Dual homing applications in transport networks
    Shew, S
    Ye, E
    FOURTH INTERNATIONAL WORKSHOP ON DESIGN OF RELIABLE COMMUNICATION NETWORKS - (DRCN 2003), PROCEEDINGS: DESIGN AND MANAGEMENT OF HIGHLY RELIABLE NETWORKS AND SERVICES, 2003, : 101 - 105
  • [43] Code Aggregate Graph: Effective Representation for Graph Neural Networks to Detect Vulnerable Code
    Nguyen, Hoang Viet
    Zheng, Junjun
    Inomata, Atsuo
    Uehara, Tetsutaro
    IEEE Access, 2022, 10 : 123786 - 123800
  • [44] ReiPool: Reinforced Pooling Graph Neural Networks for Graph-Level Representation Learning
    Luo, Xuexiong
    Zhang, Sheng
    Wu, Jia
    Chen, Hongyang
    Peng, Hao
    Zhou, Chuan
    Li, Zhao
    Xue, Shan
    Yang, Jian
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2024, 36 (12) : 9109 - 9122
  • [45] Code Aggregate Graph: Effective Representation for Graph Neural Networks to Detect Vulnerable Code
    Nguyen, Hoang Viet
    Zheng, Junjun
    Inomata, Atsuo
    Uehara, Tetsutaro
    IEEE ACCESS, 2022, 10 : 123786 - 123800
  • [46] Deep Network Embedding for Graph Representation Learning in Signed Networks
    Shen, Xiao
    Chung, Fu-Lai
    IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (04) : 1556 - 1568
  • [47] Predictive Representation Learning in Motif-Based Graph Networks
    Zhang, Kaiyuan
    Yu, Shuo
    Wan, Liangtian
    Li, Jianxin
    Xia, Feng
    AI 2019: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, 11919 : 177 - 188
  • [48] Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings
    Efthymiou, Athanasios
    Rudinac, Stevan
    Kackovic, Monika
    Worring, Marcel
    Wijnberg, Nachoem
    PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2021, 2021, : 3710 - 3719
  • [49] Graph Neural Networks with Information Anchors for Node Representation Learning
    Liu, Chao
    Li, Xinchuan
    Zhao, Dongyang
    Guo, Shaolong
    Kang, Xiaojun
    Dong, Lijun
    Yao, Hong
    MOBILE NETWORKS & APPLICATIONS, 2022, 27 (01): : 315 - 328
  • [50] Data Representation and Learning with Graph Diffusion-Embedding Networks
    Jiang, Bo
    Lin, Doudou
    Tang, Jin
    Luo, Bin
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 10406 - 10415