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A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges

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Cloud Computing (CloudComp 2021)

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Abstract

Traffic prediction plays an important role in the intelligent transportation system (ITS), because it can increase people’s travel convenience. Despite the deep neural network has been widely used in the field of traffic prediction, literature surveys of such methods and data categories are rare. In this paper, we have a summary of traffic forecasting from data, methods and challenges. Firstly, we are according to the difference of in spatio-temporal dimensions, divide the data into three types, including the spatio-temporal static data, spatial static time dynamic data, and spatio-temporal dynamic data. Secondly, we explore three significant neural networks of deep learning in traffic prediction, including the convolutional neural network (CNN), the recurrent neural network (RNN), and the hybrid neural networks models. These methods are used in many aspects of traffic prediction, including road traffic accidents forecast, road traffic flow prediction, road traffic speed forecast, and road traffic congestion forecast introduced. Finally, we provide a discussion of some current challenges and development prospects.

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Authors and Affiliations

  1. Big Data and Intelligent Engineering College, Southwest Forestry University, Kunming, 650224, China

    Pengfei Cao, Fei Dai, Guozhi Liu, Jinmei Yang & Bi Huang

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  1. Pengfei Cao
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  2. Fei Dai
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  3. Guozhi Liu
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  4. Jinmei Yang
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  5. Bi Huang
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Correspondence to Bi Huang .

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Editors and Affiliations

  1. Persian Gulf University, Bushehr, Iran

    Mohammad R. Khosravi

  2. Swinburne University of Technology, Hawthorn, VIC, Australia

    Qiang He

  3. Nanjing University, Nanjing, China

    Haipeng Dai

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Cao, P., Dai, F., Liu, G., Yang, J., Huang, B. (2022). A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges. In: Khosravi, M.R., He, Q., Dai, H. (eds) Cloud Computing. CloudComp 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-030-99191-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-99191-3_2

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