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Articles

Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting

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Pages 429-448 | Received 30 Jul 2021, Accepted 29 Oct 2021, Published online: 30 Nov 2021

References

  • Ahmed, M. S., & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transportation Research Record Journal of the Transportation Research Board, 773(722), 1–9.
  • Atwood, J., & Towsley, D. (2016). Diffusion-convolutional neural networks. In Advances in neural information processing systems (pp. 1993–2001). Morgan Kaufman.
  • Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. In 3rd International conference on learning representations, ICLR. OpenReview.net.
  • Chen, R., Liang, C. Y., Hong, W. C., & Gu, D. X. (2015). Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computing, 26, 435–443. https://doi.org/10.1016/j.asoc.2014.10.022
  • Cho, K., Merrienboer, B. V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 conference on empirical methods in natural language processing (pp. 1724–1734). ACL.
  • Chung, F. R. K., & Graham, F. C. (1997). Spectral graph theory. American Mathematical Soc.
  • Cui, Z. Y., Henrickson, K., Ke, R. M., & Wang, Y. H. (2019). Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Transactions on Intelligent Transportation Systems, 21(11), 4883–4894. https://doi.org/10.1109/TITS.2019.2950416
  • Cui, Z. Y., Ke, R. M., Pu, Z. Y., & Wang, Y. H. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. Preprint. arXiv:1801.02143
  • Dai, Y. H., & Wang, T. (2021). Prediction of customer engagement behaviour response to marketing posts based on machine learning. Connection Science, 33(4), 891–910. https://doi.org/10.1080/09540091.2021.1912710
  • Daid, R., Kumar, Y., Hu, Y. C., & Chen, W. L. (2021). An effective scheduling in data centres for efficient CPU usage and service level agreement fulfilment using machine learning. Connection Science, 33(4), 954–974. https://doi.org/10.1080/09540091.2021.1926929
  • Dauphin, Y. N., Fan, A., Auli, M., & Grangier, D. (2017). Language modeling with gated convolutional networks. In International conference on machine learning (pp. 933–941). PMLR.
  • Defferrard, M., Bresson, X., & &Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems (pp. 3837–3845). Morgan Kaufman.
  • Diao, Z. L., Wang, X., Zhang, D. F., Liu, Y. R., Xie, K., & He, S. Y. (2019). Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 890–897). AAAI Press.
  • Dong, C. J., Shao, C. F., Zhuge, C. X., & Meng, M. (2012). Spatial and temporal characteristics for congested traffic on urban expressway. Journal of Beijing University of Technology, 38(8), 1242–1246.
  • Du, B. W., Peng, H., Wang, S. Z., Bhuiyan, M. Z. A., Wang, L. H., Gong, Q. R., Liu, L., & Li, J. (2020). Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Transactions on Intelligent Transportation Systems, TITS, 21(3), 972–985. https://doi.org/10.1109/TITS.6979
  • Geng, X., Li, Y. G., Wang, L. Y., Zhang, L. Y., Yang, Q., Ye, J. P., & Liu, Y. (2019). Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 3656–3663). AAAI Press.
  • Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI conference on artificial intelligence (pp. 922–929). AAAI Press.
  • Hamed, M. M., Al-Masaeid, H. R., & Said, Z. (1995). Short-term prediction of traffic volume in urban arterials. Journal of Transportation Engineering, 121(3), 249–254. https://doi.org/10.1061/(ASCE)0733-947X(1995)121:3(249)
  • Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Advances in neural information processing systems (pp. 1024–1034). MIT Press.
  • Hinsbergen, C. P. I. J., Schreiter, T., Zuurbier, F. S., Lint, J. W. C., & Zuylen, H. J. (2012). Localized extended kalman filter for scalable real-time traffic state estimation. IEEE Transactions on Intelligent Transportation Systems, 14(1), 385–394. https://doi.org/10.1109/TITS.2011.2175728
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Huang, W., Song, G., Hong, H., & Xie, K. (2014). Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Transactions on Intelligent Transportation Systems, 15(3), 2191–2201. https://doi.org/10.1109/TITS.2014.2311123
  • Jiang, B., Zhang, Z. Y., Lin, D. D., Tang, J., & Luo, B. (2019). Semi-supervised learning with graph learning-convolutional networks. In The IEEE conference on computer vision and pattern recognition (CVPR). IEEE.
  • Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In 5th International conference on learning representations, ICLR. OpenReview.net.
  • Lee, S., & Fambro, D. (1999). Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transportation Research Record Journal of The Transportation Research Board, 1678(1), 179–188. https://doi.org/10.3141/1678-22
  • Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In 6th International conference on learning representations, ICLR. OpenReview.net.
  • Liang, W., Li, Y., Xu, J., Qin, Z., & Li, K. C. (2021). QoS prediction and adversarial attack protection for distributed services under DLaaS. IEEE Transactions on Computers, 1–14. https://doi.org/10.1109/TC.2021.3077738
  • Liang, W., Xie, S., Zhang, D., Li, X., & Li, K. C. (2020). A mutual security authentication method for RFID-PUF circuit based on deep learning. ACM Transactions on Internet Technology, 1–20. https://doi.org/10.1145/3426968
  • Liao, B., Zhang, J., Chao, W., Mcilwraith, D., & Fei, W. (2018). Deep sequence learning with auxiliary information for traffic prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, KDD (pp. 537–546). ACM.
  • Liu, W. H., Hu, E. W., Su, B. G., & Wang, J. (2021). Using machine learning techniques for DSP software performance prediction at source code level. Connection Science, 33(1), 26–41. https://doi.org/10.1080/09540091.2020.1762542
  • Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2015). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865–873. https://doi.org/10.1109/TITS.2014.2345663
  • Lv, Z. J., Xu, J. J., Zheng, K., Yin, H. Z., & Zhou, X. F. (2018). LC-RNN: A deep learning model for traffic speed prediction. In Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI (pp. 3470–3476). Mogan Kaufman.
  • Ma, X. L., Dai, Z., He, Z. B., Ma, J. H., Wang, Y., & Wang, Y. P. (2017). Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17(4), Article 818. https://doi.org/10.3390/s17040818
  • Neena, A., & Geetha, M. (2021). A scale space model of weighted average CNN ensemble for ASL fingerspelling recognition. International Journal of Computational Science and Engineering, IJCSE, 22(1), 154–161. https://doi.org/10.1504/IJCSE.2020.10029229
  • Ojeda, L. L., Schreiter, T., & CC, D. Wit. (2013). Adaptive Kalman filtering for multi-step ahead traffic flow prediction. In American control conference (ACC). IEEE.
  • Oord, A. V. D., Dieleman, S., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio. Preprint. arXiv:1609.03499
  • Peng, H., Du, B., Liu, M., Liu, M., & He, L. (2021). Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Information Sciences, 578, 401–416. https://doi.org/10.1016/j.ins.2021.07.007
  • Peng, H., Wang, H., Du, B., Bhuiyan, M., & Yu, P. S. (2020). Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Information Sciences, 521, 277–290. https://doi.org/10.1016/j.ins.2020.01.043
  • Revathy, G., Kumar, P. S., & Rajendran, V. (2021). Development of IDS using mining and machine learning techniques to estimate DoS malware. International Journal of Computational Science and Engineering, IJCSE, 24(3), 259–275. https://doi.org/10.1504/IJCSE.2021.115646
  • Srivastava, V., & Biswas, B. (2020). CNN-based salient features in HSI image semantic target prediction. Connection Science, 32(2), 113–131. https://doi.org/10.1080/09540091.2019.1650330
  • Sun, S. L., Zhang, C. S., & Yu, G. Q. (2006). A Bayesian network approach to traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems, 7(1), 124–132. https://doi.org/10.1109/TITS.2006.869623
  • Tang, C., Sun, J., & Sun, Y. (2020). Dynamic spatial-temporal graph attention graph convolutional network for short-term traffic flow forecasting. In IEEE international symposium on circuits and systems, ISCAS (pp. 1–5). IEEE.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems 30: Annual conference on neural information processing systems (pp. 5998–6008). MIT Press.
  • Voort, M. V. R., Dougherty, M., & Watson, S. (1996). Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies, 4(5), 307–318. https://doi.org/10.1016/S0968-090X(97)82903-8
  • Williams, B. M., & Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering, 129(6), 664–672. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)
  • Wu, Z. H., Pan, S. R., Long, G. D., Jiang, J., & Zhang, C. Q. (2019). Graph wavenet for deep spatial-temporal graph modeling. In Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI (pp. 1907–1913). Morgan Kaufman.
  • Yao, H., Fei, W., Ke, J., Tang, X., & Ye, J. (2018). Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the AAAI conference on artificial intelligence (pp. 2588–2595). AAAI Press.
  • Yu, B., Li, M., Zhang, J., & Zhu, Z. (2019). 3D graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting. arXiv:1903.00919. CoRR.
  • Yu, B., Yin, H. T., & Zhu, Z. X. (2018). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI (pp. 3634–3640). Morgan Kaufman.
  • Zhang, J. B., Zheng, Y., & Qi, D. K. (2017). Deep spatio-temporal residual networks for citywide crowd flows prediction. In Thirty-first AAAI conference on artificial intelligence. AAAI Press.
  • Zhang, L., Liu, Q., Yang, W., Wei, N., & Dong, D. (2013). An improved K-nearest neighbor model for short-term traffic flow prediction. Procedia – Social and Behavioral Sciences, 96(2013), 653–662. https://doi.org/10.1016/j.sbspro.2013.08.076
  • Zhao, L., Song, Y. J., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. H. F. (2019). T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, PP(99), 1–11. https://doi.org/10.1109/TITS.2019.2935152