A physics-informed deep learning paradigm for car-following models

被引：0
|
作者：
Mo, Zhaobin [1 ]
Shi, Rongye [1 ]
Di, Xuan [1 ,2 ]
机构：
[1] Department of Civil Engineering and Engineering Mechanics, Columbia University, United States
[2] Data Science Institute, Columbia University, United States
来源：
Transportation Research Part C: Emerging Technologies | 2021年 / 130卷
关键词：
67;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [1] A physics-informed deep learning paradigm for car-following models
    Mo, Zhaobin
    Shi, Rongye
    Di, Xuan
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 130
  • [2] A quantile-regression physics-informed deep learning for car-following model
    Liu, Jiaxin
    Jiang, Rui
    Zhao, Jiandong
    Shen, Wei
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2023, 154
  • [3] A Car-Following Model considering the Effect of Following Vehicles under the Framework of Physics-Informed Deep Learning
    Xu, Le
    Ma, Jianxiao
    Wang, Yuchen
    [J]. Journal of Advanced Transportation, 2022, 2022
  • [4] A Car-Following Model considering the Effect of Following Vehicles under the Framework of Physics-Informed Deep Learning
    Xu, Le
    Ma, Jianxiao
    Wang, Yuchen
    [J]. JOURNAL OF ADVANCED TRANSPORTATION, 2022, 2022
  • [5] A Physics-Informed Generative Car-Following Model for Connected Autonomous Vehicles
    Ma, Lijing
    Qu, Shiru
    Song, Lijun
    Zhang, Zhiteng
    Ren, Jie
    [J]. ENTROPY, 2023, 25 (07)
  • [6] Physics-informed deep reinforcement learning-based integrated two-dimensional car-following control strategy for connected automated vehicles
    Shi, Haotian
    Zhou, Yang
    Wu, Keshu
    Chen, Sikai
    Ran, Bin
    Nie, Qinghui
    [J]. KNOWLEDGE-BASED SYSTEMS, 2023, 269
  • [7] Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models
    Shi, Rongye
    Mo, Zhaobin
    Di, Xuan
    [J]. THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 540 - 547
  • [8] A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation
    Shi, Rongye
    Mo, Zhaobin
    Huang, Kuang
    Di, Xuan
    Du, Qiang
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (08) : 11688 - 11698
  • [9] A Physics-Informed Deep Learning Paradigm for Transient Power Angle Stability Assessment
    Li, Xiang
    Chen, Siyuan
    Zhang, Jun
    Gao, Jiemai
    Bai, Yuyang
    [J]. IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION, 2022, 6 : 948 - 952
  • [10] Physics-informed deep learning for digital materials
    Zhang, Zhizhou
    Gu, Grace X.
    [J]. THEORETICAL AND APPLIED MECHANICS LETTERS, 2021, 11 (01)
12345