Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Yuxiu Hua, Zhifeng Zhao, Zhiming Liu, Xianfu Chen, Rongpeng Li, Honggang Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    5 Citations (Scopus)

    Abstract

    Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.

    Original languageEnglish
    Title of host publication2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages6
    ISBN (Electronic)978-1-5386-6358-5, 978-1-5386-6357-8
    ISBN (Print)978-1-5386-6359-2
    DOIs
    Publication statusPublished - 2018
    MoE publication typeA4 Article in a conference publication
    Event88th IEEE Vehicular Technology Conference, VTC-Fall 2018 - Chicago, United States
    Duration: 27 Aug 201830 Aug 2018
    Conference number: 88

    Publication series

    SeriesIEEE Vehicular Technology Conference Papers
    Volume2018-August
    ISSN1090-3038

    Conference

    Conference88th IEEE Vehicular Technology Conference, VTC-Fall 2018
    Abbreviated titleVTC-Fall 2018
    CountryUnited States
    CityChicago
    Period27/08/1830/08/18

    Keywords

    • big data
    • deep learning
    • LSTM
    • random connectivity
    • RNN
    • Traffic prediction

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  • Cite this

    Hua, Y., Zhao, Z., Liu, Z., Chen, X., Li, R., & Zhang, H. (2018). Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory. In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall) [8690851] IEEE Institute of Electrical and Electronic Engineers. IEEE Vehicular Technology Conference Papers, Vol.. 2018-August https://doi.org/10.1109/VTCFall.2018.8690851