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 language | English |
|---|---|
| Title of host publication | 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall) |
| Publisher | Institute of Electrical and Electronic Engineers IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5386-6358-5, 978-1-5386-6357-8 |
| ISBN (Print) | 978-1-5386-6359-2 |
| DOIs | |
| Publication status | Published - 2018 |
| MoE publication type | A4 Article in a conference publication |
| Event | 88th IEEE Vehicular Technology Conference, VTC-Fall 2018 - Chicago, United States Duration: 27 Aug 2018 → 30 Aug 2018 Conference number: 88 |
Publication series
| Name | IEEE Vehicular Technology Conference |
|---|---|
| Volume | 2018-August |
| ISSN (Print) | 1090-3038 |
| ISSN (Electronic) | 2577-2465 |
Conference
| Conference | 88th IEEE Vehicular Technology Conference, VTC-Fall 2018 |
|---|---|
| Abbreviated title | VTC-Fall 2018 |
| Country | United States |
| City | Chicago |
| Period | 27/08/18 → 30/08/18 |
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Keywords
- big data
- deep learning
- LSTM
- random connectivity
- RNN
- Traffic prediction
Cite this
}
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). Institute of Electrical and Electronic Engineers IEEE, 2018. 8690851 (IEEE Vehicular Technology Conference Papers, Vol. 2018-August).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
AU - Hua, Yuxiu
AU - Zhao, Zhifeng
AU - Liu, Zhiming
AU - Chen, Xianfu
AU - Li, Rongpeng
AU - Zhang, Honggang
PY - 2018
Y1 - 2018
N2 - 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.
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.
KW - big data
KW - deep learning
KW - LSTM
KW - random connectivity
KW - RNN
KW - Traffic prediction
UR - http://www.scopus.com/inward/record.url?scp=85064888832&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2018.8690851
DO - 10.1109/VTCFall.2018.8690851
M3 - Conference article in proceedings
SN - 978-1-5386-6359-2
T3 - IEEE Vehicular Technology Conference
BT - 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)
PB - Institute of Electrical and Electronic Engineers IEEE
ER -