Enhancing transportation systems via deep learning: A survey

被引:187
|
作者
Wang, Yuan [1 ]
Zhang, Dongxiang [2 ]
Liu, Ying [2 ]
Dai, Bo [2 ]
Lee, Loo Hay [1 ]
机构
[1] Natl Univ Singapore, Dept Ind Syst Engn & Management, Singapore, Singapore
[2] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
Deep learning; Transportation systems; Survey; TRAFFIC FLOW PREDICTION; CONVOLUTIONAL NEURAL-NETWORK; FORECASTING TOURISM DEMAND; TRAVEL-TIME PREDICTION; VEHICLE DETECTION; SIGN RECOGNITION; BELIEF NETWORKS; MODEL; IMAGES; DYNAMICS;
D O I
10.1016/j.trc.2018.12.004
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Machine learning (ML) plays the core function to intellectualize the transportation systems. Recent years have witnessed the advent and prevalence of deep learning which has provoked a storm in ITS (Intelligent Transportation Systems). Consequently, traditional ML models in many applications have been replaced by the new learning techniques and the landscape of ITS is being reshaped. Under such perspective, we provide a comprehensive survey that focuses on the utilization of deep learning models to enhance the intelligence level of transportation systems. By organizing multiple dozens of relevant works that were originally scattered here and there, this survey attempts to provide a clear picture of how various deep learning models have been applied in multiple transportation applications.
引用
收藏
页码:144 / 163
页数:20
相关论文
共 50 条
  • [1] Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey
    Haydari, Ammar
    Yilmaz, Yasin
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (01) : 11 - 32
  • [2] Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends
    Veres, Matthew
    Moussa, Medhat
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 21 (08) : 3152 - 3168
  • [3] Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends
    Veres, Matthew
    Moussa, Medhat
    [J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21 (08): : 3152 - 3168
  • [4] Distributed Learning in Intelligent Transportation Systems: A Survey
    Li, Qiong
    Zhou, Wanlei
    Zheng, Xi
    [J]. Information (Switzerland), 2024, 15 (09)
  • [5] Deep learning support for intelligent transportation systems
    Guerrero-Ibanez, J.
    Contreras-Castillo, J.
    Zeadally, S.
    [J]. TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2021, 32 (03)
  • [6] Applications of Deep Learning in Intelligent Transportation Systems
    Arya Ketabchi Haghighat
    Varsha Ravichandra-Mouli
    Pranamesh Chakraborty
    Yasaman Esfandiari
    Saeed Arabi
    Anuj Sharma
    [J]. Journal of Big Data Analytics in Transportation, 2020, 2 (2): : 115 - 145
  • [7] Deep reinforcement learning for transportation network combinatorial optimization: A survey
    Wang, Qi
    Tang, Chunlei
    [J]. KNOWLEDGE-BASED SYSTEMS, 2021, 233
  • [8] A Survey of Recommender Systems Based on Deep Learning
    Mu, Ruihui
    [J]. IEEE ACCESS, 2018, 6 : 69009 - 69022
  • [9] Detecting Defects in Deep Learning Systems: a Survey
    Xing, Shangyu
    Zhou, Junjie
    Zhu, Fukang
    Yang, Xiaowen
    Wang, Yu
    Wang, Linzhang
    [J]. 13TH ASIA-PACIFIC SYMPOSIUM ON INTERNETWARE, INTERNETWARE 2022, 2022, : 137 - 146
  • [10] Applications of Deep Learning for Vehicle Detection for Smart Transportation Systems
    Sharma, Poonam
    Singh, Akansha
    Dhull, Anuradha
    [J]. PROCEEDINGS OF ACADEMIA-INDUSTRY CONSORTIUM FOR DATA SCIENCE (AICDS 2020), 2022, 1411 : 307 - 321