References
- 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. https://doi.org/10.1109/TIT.2003.813506
- 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.
- 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. https://doi.org/10.1137/S1064827595287997
- 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. https://doi.org/10.1162/neco.2006.18.7.1527
- 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.
- 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.
- K-indicator(2020), https://www.index.go.kr/
- Kipf, T. N. and Welling, M.(2016), "Semi-Supervised Classification with Graph Convolutional Networks," arXiv preprint arXiv:1609.02907.
- LeCun, Y., Bengio, Y. and Hinton, G.(2015), "Deep Learning," Nature, vol. 521, no. 7553, pp.436-444. https://doi.org/10.1038/nature14539
- 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
- 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.
- 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.
- 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. https://doi.org/10.1177/0361198120930010
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.