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 Consequently, traditional ML models in many applications have been replaced by the new learning techniques and the landscape of ITS is being reshaped.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
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