Abstract
Spatial-temporal traffic flow prediction is beneficial for controlling traffic and saving traffic time. Researchers have proposed prediction models based on spatial-temporal representation learning. Although these models have achieved better performance than traditional methods, they seldom consider several essential aspects: 1) distances and directions from the spatial aspect, 2) the bi-relation among historical time intervals from the temporal aspect, and 3) missing historical traffic data, which leads to an imprecise spatial-temporal features extraction. To this end, we propose Fine-Grained Features learning based on Transformer-encoder and Graph convolutional networks (FGFTG) to improve the performance of traffic flow prediction in a missing data scenario. FGFTG consists of two components: feature extractors and a data completer. The feature extractors learn fine-grained spatial-temporal representations from spatial and temporal perspectives. They extract smoother representation with the information of distance and direction from a spatial perspective based on graph convolutional networks and node2vec and achieve bidirectional learning for temporal perspective utilizing transformer encoder. The data completer simulates the traffic flow data distribution and generates reliable data to fill in missing data based on generative adversarial networks. Experiments on two public datasets demonstrate the effectiveness of our approach over the state-of-the-art methods.
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References
Abadi, A., Rajabioun, T., Ioannou, P.A.: Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst 16(2), 653–662 (2015)
Ang, A., Piazzesi, M.: A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. J. Monet. Econ. 50(4), 745–787 (1999)
Beirão, G., Cabral, J.S.: Understanding attitudes towards public transport and private car: a qualitative study. Transp. policy 14(6), 478–489 (2007)
Cao, L., Ma, K., Cao, B., Fan, J.: Forecasting long-term call traffic based on seasonal dependencies. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds.) CollaborateCom 2019. LNICST, vol. 292, pp. 231–246. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30146-0_16
Chen, C., Wang, Y., Li, L., Hu, J., Zhang, Z.: The retrieval of intra-day trend and its influence on traffic prediction. Transp. Res. Part C Emerg. Technol 22, 103–118 (2012)
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. arXiv preprint arXiv:1606.09375 (2016)
Deng, D., Shahabi, C., Demiryurek, U., Zhu, L., Yan, L.: Latent space model for road networks to predict time-varying traffic. In: ACM Sigkdd International Conference (2016)
Drew, D.R.: Traffic flow theory and control. McGraw-Hill Series in Transportation 316 (1968)
Fan, Z., Xuan, S., Shibasaki, R., Adachi, R.: Citymomentum: an online approach for crowd behavior prediction at a citywide level. In: the 2015 ACM International Joint Conference (2015)
Feng, N., Guo, S., Song, C., Zhu, Q., Wan, H.: Multi-component spatial-temporal graph convolution networks for traffic flow forecasting. J. Softw 30(3), 759–769 (2019)
Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328. IEEE (2016)
Geng, X., Li, Y., Wang, L., Zhang, L., Liu, Y.: Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3656–3663 (2019)
Ghahramani, Z., Jordan, M.I.: Supervised learning from incomplete data via an em approach. In: Advances in Neural Information Processing Systems, pp. 120–127 (1994)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: The 22nd ACM SIGKDD International Conference (2016)
Hoang, M.X., Yu, Z., Singh, A.K.: FCCF: forecasting citywide crowd flows based on big data. In: the 24th ACM SIGSPATIAL International Conference (2016)
Kang, D., Lv, Y., Chen, Y.Y.: Short-term traffic flow prediction with LSTM recurrent neural network. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6. IEEE (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2017). https://openreview.net/forum?id=SJU4ayYgl
Klema, V., Laub, A.: The singular value decomposition: its computation and some applications. IEEE Trans. Autom. Control 25(2), 164–176 (1980)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Li, L., Li, Y., Li, Z.: Efficient missing data imputing for traffic flow by considering temporal and spatial dependence. Transp. Res. Part C Emerg. Technol. 34, 108–120 (2013)
Li, T., Zhang, J., Bao, K., Liang, Y., Li, Y., Zheng, Y.: Autost: efficient neural architecture search for spatio-temporal prediction. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 794–802 (2020)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting (2017)
Li, Y., Zheng, Y., Zhang, H., Chen, L.: Traffic prediction in a bike-sharing system. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10 (2015)
Lin, Z., Feng, J., Lu, Z., Li, Y., Jin, D.: Deepstn+: context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1020–1027 (2019)
Lippi, M., Bertini, M., Frasconi, P.: Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans. Intell. Transp. Syst. 14(2), 871–882 (2013)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2014)
Ma, X., Zhuang, D., He, Z., Ma, J., Wang, Y.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818 (2017)
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J.: Predicting taxi-passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 14(3), 1393–1402 (2013)
Polson, N.G., Sokolov, V.O.: Deep learning for short-term traffic flow prediction. Transp. Res. Part C Emerg. Technol. 79, 1–17 (2017)
Shekhar, S., Williams, B.M.: Adaptive seasonal time series models for forecasting short-term traffic flow. Transp. Res. Rec. 2024(1), 116–125 (2007)
Silva, R., Kang, S.M., Airoldi, E.M.: Predicting traffic volumes and estimating the effects of shocks in massive transportation systems. Proc. Natl. Acad. Sci. United States Am. 112(18), 5643–8 (2015)
Sun, J., Zhang, J., Li, Q., Yi, X., Zheng, Y.: Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. IEEE Trans. Knowl. Data Eng. (99), 1 (2020)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wei, L., Zheng, Y., Chawla, S., Yuan, J., Xing, X.: Discovering spatio-temporal causal interactions in traffic data streams (2011)
Wei, W., Wu, H., Ma, H.: An autoencoder and LSTM-based traffic flow prediction method. Sensors 19(13), 2946 (2019)
Li, X., et al.: Prediction of urban human mobility using large-scale taxi traces and its applications. Front. Comput. Sci. 6(001), 111–121 (2012)
Xu, J., Zhang, Y., Jia, Y., Xing, C.: An efficient traffic prediction model using deep spatial-temporal network. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds.) CollaborateCom 2018. LNICST, vol. 268, pp. 386–399. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12981-1_27
Xu, Y., Kong, Q.J., Klette, R., Liu, Y.: Accurate and interpretable bayesian mars for traffic flow prediction. IEEE Trans. Intell. Transp. Syst 15(6), 2457–2469 (2014)
Xuan, S., Zhang, Q., Sekimoto, Y., Shibasaki, R.: Prediction of human emergency behavior and their mobility following large-scale disaster. ACM (2014)
Yang, B., Sun, S., Li, J., Lin, X., Tian, Y.: Traffic flow prediction using LSTM with feature enhancement. Neurocomputing 332, 320–327 (2019)
Yao, H., Fei, W., Ke, J., Tang, X., Ye, J.: Deep multi-view spatial-temporal network for taxi demand prediction (2018)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting (2017)
Yu, H.F., Rao, N., Dhillon, I.S.: Temporal regularized matrix factorization for high-dimensional time series prediction. In: NIPS, pp. 847–855 (2016)
Yu, R., Li, Y., Shahabi, C., Demiryurek, U., Yan, L.: Deep Learning: a generic approach for extreme condition traffic forecasting. In: Proceedings of the 2017 SIAM International Conference on Data Mining (2017)
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Thirty-first AAAI conference on artificial intelligence (2017)
Zhang, N., Wang, F.Y., Zhu, F., Zhao, D., Tang, S.: Dynacas: computational experiments and decision support for ITS. IEEE Intell. Syst 23(6), 19–23 (2008)
Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1234–1241 (2020)
Zheng, Z., Yang, Y., Liu, J., Dai, H.N., Zhang, Y.: Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Trans. Intell. Transp. Syst. 20(10), 3927–3939 (2019)
Acknowledgments
This research was supported by the National Key Research and Development Program of China (2020YFB1712903), the Research Program of Chongqing Technology Innovation and Application Development (CSTC2019jscx-zdztzxX0031 and cstc2020kqjscx-phxm1304), and the Overseas Returnees Innovation and Entrepreneurship Support Program of Chongqing (cx2020097).
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Wang, S., Gao, M., Wang, Z., Wang, J., Wu, F., Wen, J. (2021). Fine-Grained Spatial-Temporal Representation Learning with Missing Data Completion for Traffic Flow Prediction. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-92635-9_9
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