Browse > Article
http://dx.doi.org/10.12815/kits.2021.20.1.70

Traffic Speed Prediction Based on Graph Neural Networks for Intelligent Transportation System  

Kim, Sunghoon (Department of Data Science, Seoul Women's University)
Park, Jonghyuk (Department of Industrial Engineering, Seoul National University)
Choi, Yerim (Department of Data Science, Seoul Women's University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.1, 2021 , pp. 70-85 More about this Journal
Abstract
Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.
Keywords
Intelligent transport systems; Traffic forecasting; Graph neural network; Jensen-Shannon divergence; Spectral clustering;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Zhao, Z., Chen, W., Wu, X., Chen, P. C. and Liu, J.(2017), "LSTM Network: A Deep Learning Approach for Short-Term Traffic Forecast," IET Intelligent Transport Systems, vol. 2, no. 11, pp.68-75.
2 Endres, D. M. and Schindelin, J. E.(2003), "A New Metric for Probability Distributions," IEEE Transactions on Information Theory, vol. 49, no. 7, pp.1858-1860.   DOI
3 Fu, R., Zhang, Z. and Li, L.(2016), "Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction," Youth Academic Annual Conference of Chinese Association of Automation, Wuhan, China, pp.324-328.
4 George, K. and Vipin, K.(1998), "A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs," SIAM Journal on scientific Computing, vol. 20, no. 1, pp.359-392.   DOI
5 Hinton, G. E., Osindero, S. and Teh, Y. W.(2006), "A Fast Learning Algorithm for Deep Belief Nets," Neural Computation, vol. 18, no. 7, pp.1527-1554.   DOI
6 D. Kim, K. Hwang and Y. Yoon,(2019), "Prediction of Traffic Congestion in Seoul by Deep Neural Network," The Journal of the Korea Institute of Intelligent Transport Systems, vol. 18, no. 4, pp.44-57.
7 Y. Kim, J. Kim, Y, Han, J. Kim and J. Hwang,(2020), "Development of Traffic Speed Prediction Model Reflecting Spatio-Temporal Impact Based on Deep Neural Network," The Journal of the Korea Institute of Intelligent Transport Systems, vol. 19, no. 1, pp.1-16.
8 K-indicator(2020), https://www.index.go.kr/
9 Kipf, T. N. and Welling, M.(2016), "Semi-Supervised Classification with Graph Convolutional Networks," arXiv preprint arXiv:1609.02907.
10 LeCun, Y., Bengio, Y. and Hinton, G.(2015), "Deep Learning," Nature, vol. 521, no. 7553, pp.436-444.   DOI
11 Li, Y., Yu, R., Shahabi, C. and Liu, Y.(2018), "Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting," The International Conference on Learning Representations. Vancouver, Canada
12 Lv, Z., Xu, J., Zheng, K., Yin, H., Zhao, P. and Zhou, X.(2018), "Lc-rnn: A Deep Learning Model for Traffic Speed Prediction," International Joint Conferences on Artificial Intelligence, Stockholm, Sweden, pp.3470-3476.
13 Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y. and Wang, Y.(2017), "Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction," Sensors, vol. 4, no. 17, p.818.
14 Mallick, T., Balaprakash, P., Rask, E. and Macfarlane, J.(2020), "Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting," Transportation Research Record, vol. 2674, No. 9, pp.473-488.   DOI
15 Oord, A. Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A. and Kavukcuoglu, K.(2016), "Wavenet: A Generative Model for Raw Audio," arXiv preprint arXiv:1609.03499.
16 C. Park, C. Lee, H. Bahng, K. Kim, S. Jin, S. Ko and J. Choo,(2019), "STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting," arXiv preprint arXiv:1911.13181.
17 Yu, B., Yin, H. and Zhu, Z.(2018), "Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting," International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp.3634-3640.
18 Wang, J., Gu, Q., Wu, J., Liu, G. and Xiong, Z.(2016), "Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method," IEEE 16th International Conference on Data Mining, Barcelona, Spain, pp.499-508.
19 Wu, Z., Pan, S., Long, G., Jiang, J. and Zhang, C.(2019), "Graph Wavenet for Deep Spatial-Temporal Graph Modeling," International Joint Conference on Artificial Intelligence, Macao, China.
20 Lv, Y., Duan, Y., Kang, W., Li, Z. and Wang, F. Y.(2014), "Traffic Flow Prediction with Big Data: A Deep Learning Approach," IEEE Transactions on Intelligent Transportation Systems, vol. 2, no. 16, pp.865-873.
21 Yu, H., Wu, Z., Wang, S., Wang, Y. and Ma, X.(2017), "Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks," Sensors, vol. 7, no. 17, p.1501.
22 Zhang, J., Shi, X., Xie, J., Ma, H., King, I. and Yeung, D. Y.(2018), "Gaan: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs," Uncertainty in Artificial Intelligence, California, USA.