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

    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

    Fingerprint

    Memory Term
    Connectivity
    Traffic
    Prediction
    Baseline
    Telecommunication Network
    Recurrent neural networks
    Training Samples
    Recurrent Neural Networks
    Learning
    Long short-term memory
    Deep learning
    Neurons
    Telecommunication networks
    Latency
    Computational Cost
    Neuron
    Neural Networks
    Model-based
    Neural networks

    Keywords

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

    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
    Hua, Yuxiu ; Zhao, Zhifeng ; Liu, Zhiming ; Chen, Xianfu ; Li, Rongpeng ; Zhang, Honggang. / Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory. 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall). IEEE Institute of Electrical and Electronic Engineers , 2018. (IEEE Vehicular Technology Conference Papers, Vol. 2018-August).
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    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.",
    keywords = "big data, deep learning, LSTM, random connectivity, RNN, Traffic prediction",
    author = "Yuxiu Hua and Zhifeng Zhao and Zhiming Liu and Xianfu Chen and Rongpeng Li and Honggang Zhang",
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    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, 88th IEEE Vehicular Technology Conference, VTC-Fall 2018, Chicago, United States, 27/08/18. https://doi.org/10.1109/VTCFall.2018.8690851

    Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory. / Hua, Yuxiu; Zhao, Zhifeng; Liu, Zhiming; Chen, Xianfu; Li, Rongpeng; Zhang, Honggang.

    2018 IEEE 88th Vehicular Technology Conference (VTC-Fall). IEEE Institute of Electrical and Electronic Engineers , 2018. 8690851 (IEEE Vehicular Technology Conference Papers, Vol. 2018-August).

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

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    AU - Zhao, Zhifeng

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    AU - Chen, Xianfu

    AU - Li, Rongpeng

    AU - Zhang, Honggang

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    AB - 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.

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    KW - random connectivity

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    M3 - Conference article in proceedings

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