Abstract
Traffic flow forecasting is significant to traffic management and public safety. However, it is a challenging problem, because of complex spatial and temporal dependencies. Many existing approaches adopt Graph Convolution Networks (GCN) to model spatial dependencies and recurrent neural networks (RNN) to model temporal dependencies, simultaneously. However, the existing approaches mainly use adjacency matrix or distance matrix to represent the correlations between adjacent road segments, which fail to capture dynamic spatial dependencies. Besides, these approaches ignore the lag influence caused by propagation times of traffic flows and cannot model the global aggregation effect of traffic flows. In response to the limitations of the existing approaches, we model local aggregation and global aggregation of traffic flows. We propose a novel model, called the Local and Global Spatial Temporal Network (LGSTN), to forecast the traffic flows on a road segment basis (instead of regions). We first construct time-dependent flow transfer graphs to capture dynamic spatial correlations among the local traffic flows of the adjacent road segments. Next, we adopt spatial-based GCNs to model local traffic flow aggregation. Then, we propose a Lag-gated LSTM to model global traffic flow aggregation by considering free-flow reachable time matrix. Experiments on two real-world datasets have demonstrated our proposed LGSTN considerably outperforms state-of-the-art traffic forecast methods.
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References
Chen, C., Li, K., Teo, S.G., Zou, X., Wang, K., Wang, J., Zeng, Z.: Gated residual recurrent graph neural networks for traffic prediction. Proc. AAAI Conf. Artif. Intell. 33, 485–492 (2019)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. Computer Science (2014)
Cui, Z., Henrickson, K., Ke, R., Wang, Y.: High-order graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. CoRR abs/1802.07007 (2018)
Engström, R.: The roads’ role in the freight transport system. Transp. Res. Procedia 14, 1443–1452 (2016)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc. AAAI Conf. Artif. Intell. 33, 922–929 (2019)
Jabbarpour, M.R., Zarrabi, H., Khokhar, R.H., Shamshirband, S., Choo, K.-K.R.: Applications of computational intelligence in vehicle traffic congestion problem: a survey. Soft Comput. 22(7), 2299–2320 (2017). https://doi.org/10.1007/s00500-017-2492-z
Jeong, Y.S., Byon, Y.J., Castro-Neto, M.M., Easa, S.M.: Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 14(4), 1700–1707 (2013)
Khetarpaul, S., Gupta, S.K., Subramaniam, L.V.: Analyzing travel patterns for scheduling in a dynamic environment. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 304–318. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40511-2_21
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems (2012)
Li, X., et al.: Prediction of urban human mobility using large-scale taxi traces and its applications. Front. Comput. Sci. China 6(1), 111–121 (2012)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Lv, Z., Xu, J., Zheng, K., Yin, H., Zhao, P., Zhou, X.: LC-RNN: A deep learning model for traffic speed prediction. In: IJCAI, pp. 3470–3476 (2018)
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818 (2017)
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transport. Res. Part C 54, 187–197 (2015)
May, M., Hecker, D., Korner, C., Scheider, S., Schulz, D.: A vector-geometry based spatial KNN-algorithm for traffic frequency predictions. In: IEEE International Conference on Data Mining Workshops (2008)
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Predicting taxi-passenger demand using streaming data. IEEE Trans. Intell. Transport. Syst. 14(3), 1393–1402 (2013)
Pan, B., Demiryurek, U., Shahabi, C.: Utilizing real-world transportation data for accurate traffic prediction. In: 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, 10–13 December 2012, pp. 595–604 (2012)
Shi, X., Chen, Z., Hao, W., Yeung, D.Y., Wong, W., Woo, W.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: International Conference on Neural Information Processing Systems (2015)
Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: AAAI Conference on Artificial Intelligence (2020)
Tao, Y., Sun, P., Boukerche, A.: A novel travel-delay aware short-term vehicular traffic flow prediction scheme for vanet. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2019)
Wu, Z., Li, J., Yu, J., Zhu, Y., Xue, G., Li, M.: L3: Sensing driving conditions for vehicle lane-level localization on highways. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 (2019)
Xie, Y., Zhang, Y., Ye, Z.: Short?term traffic volume forecasting using kalman filter with discrete wavelet decomposition. Comput. Aided Civil Infrastructure Eng. 22(5), 326–334 (2010)
Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: AAAI Conference on Artificial Intelligence (2019)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
Yu, J., et al.: Sensing human-screen interaction for energy-efficient frame rate adaptation on smartphones. IEEE Trans. Mob. Comput. 14(8), 1698–1711 (2014)
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)
Acknowledgements
This research is supported in part by the 2030 National Key AI Program of China 2018AAA0100503 (2018AAA0100500), National Science Foundation of China (No. 61772341, No. 61472254, No. 61772338 and No. 61672240), Shanghai Municipal Science and Technology Commission (No. 18511103002, No. 19510760500, and No. 19511101500), the Innovation and Entrepreneurship Foundation for oversea high-level talents of Shenzhen (No. KQJSCX20180329191021388), the Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, the Program for Shanghai Top Young Talents, Shanghai Engineering Research Center of Digital Education Equipment, and SJTU Global Strategic Partnership Fund (2019 SJTU-HKUST).
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Qu, Y., Zhu, Y., Zang, T., Xu, Y., Yu, J. (2020). Modeling Local and Global Flow Aggregation for Traffic Flow Forecasting. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_30
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DOI: https://doi.org/10.1007/978-3-030-62005-9_30
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