참고문헌 (22)

1. 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. 

   상세보기

2. 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. 

3. 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. 

   상세보기

4. 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. 

   상세보기

5. 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. 

6. 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. 

7. K-indicator(2020), https://www.index.go.kr/ 

8. Kipf, T. N. and Welling, M.(2016), "Semi-Supervised Classification with Graph Convolutional Networks," arXiv preprint arXiv:1609.02907. 

9. LeCun, Y., Bengio, Y. and Hinton, G.(2015), "Deep Learning," Nature, vol. 521, no. 7553, pp.436-444. 

   상세보기

10. 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 

11. 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. 

12. 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. 

13. 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. 

14. 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. 

15. 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. 

16. 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. 

17. 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. 

18. 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. 

19. 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. 

20. 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. 

21. 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. 

22. 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.