Description of macroscopic relationships among traffic flow variables using neural network models

被引：0
|
作者：
Nakatsuji, Takashi [1 ]
Tanaka, Mitsuru [1 ]
Nasser, Pourmoallem [1 ]
Hagiwara, Toru [1 ]
机构：
[1] Hokkaido Univ, Sapporo, Japan
来源：
Transportation Research Record | 1995年 / 1510期
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
下载
收藏
页码：11 / 18
相关论文
共 50 条
  • [21] Comparison of numerical schemes for macroscopic traffic flow models
    Ngoduy, D
    Hoogendoorn, SP
    Van Zuylen, HJ
    CALIBRATION AND VALIDATION OF SIMULATION MODELS 2004, 2004, (1876): : 52 - 61
  • [22] Traffic Flow Estimation using Graph Neural Network with Aggregation of Traffic Features
    Putri, Adiyana
    Brahmana, Ferio
    Joelianto, Endra
    Sutarto, Herman Y.
    2022 17TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV), 2022, : 972 - 977
  • [23] Network traffic prediction using ARIMA and neural networks models
    Rutka, G.
    ELEKTRONIKA IR ELEKTROTECHNIKA, 2008, (04) : 47 - 52
  • [24] Dynamic prediction of traffic flow by using backpropagation neural network
    Zhu, Z
    Yang, ZS
    TRAFFIC AND TRANSPORTATION STUDIES, 1998, : 548 - 555
  • [25] Study on traffic flow prediction using RBF neural network
    Xiao, JM
    Wang, XH
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 2672 - 2675
  • [26] GRAPHICAL REPRESENTATION OF CAUSE-EFFECT RELATIONSHIPS AMONG CHEMICAL PROCESS VARIABLES USING A NEURAL NETWORK APPROACH
    De Almeida, Gustavo M.
    Cardoso, Marcelo
    Rena, Danilo C.
    Park, Song W.
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2010, 9 (01) : 69 - 86
  • [27] Graphical Representation of Cause-Effect Relationships among Chemical Process Variables using a Neural Network Approach
    de Almeida, Gustavo M.
    Cardoso, Marcelo
    Rena, Danilo C.
    Park, Song W.
    2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8, 2008, : 2668 - +
  • [28] A macroscopic traffic flow modelling and collision avoidance using B-spline and Physics-Informed Neural Network approaches
    Haddioui M.
    Qaraai Y.
    Bouarafa S.
    Agoujil S.
    Bouhamidi A.
    International Journal of Intelligent Networks, 2024, 5 : 196 - 203
  • [29] Real-time travel time estimation using macroscopic traffic flow models
    Kachroo, P
    Ozbay, K
    Hobeika, AG
    2001 IEEE INTELLIGENT TRANSPORTATION SYSTEMS - PROCEEDINGS, 2001, : 132 - 137
  • [30] Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
    Thodi, Bilal Thonnam
    Ambadipudi, Sai Venkata Ramana
    Jabari, Saif Eddin
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2024, 160
12345