Abstract
Traffic flow prediction is of great importance for traffic management. However, most existing researches only focus on region flow or road segment flow (vertex value) prediction, and the transit flow (edge weight) prediction is largely untouched. Compared to region flow and road segment flow prediction, transit flow prediction is more challenging in that 1) the transit flow between pairs of regions has complex spatial-temporal dependencies, and 2) it has larger changes over time due to the large number of region pairs. To address these issues, in this paper we define the transit flow as edges in directed graphs and formulate the transit flow prediction problem as a dynamic weighted link prediction problem. We propose a deep learning based method called Spatial-Temporal Network (STN) to make an accurate prediction of the transit flow. The STN model combines graph convolutional network (GCN) and long short-term memory (LSTM) to capture the dynamic spatial-temporal correlations. To capture the static topological structure, the neighborhood relation graph is adopted as an auxiliary graph to improve the prediction accuracy, and a two-stage-skip strategy is adopted to allow edge features reused which makes the STN focus more on the edge values compared to simple GCN modeling. We conduct the proposed STN model and verify its effectiveness in transit flow prediction on two real-world taxi datasets. Experiments demonstrate that our model reduces the prediction RMSE error by approximately 15.88%–52.48% on real-world datasets compared to state-of-the-art methods.
This work was supported in part by the Natural Science Foundation of Guangdong under Grant 2021A1515011578, Natural Science Foundation of China under Grant 61672441 and Grant 61673324, Natural Science Foundation of Fujian under Grant 2018J01097, Shenzhen Basic Research Program under Grant JCYJ20170818141325209 and Grant JCYJ20190809161603551.
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Ahmed, M.S., Cook, A.R.: Analysis of freeway traffic time-series data by using Box-Jenkins techniques, vol. 722 (1979)
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
Bonner, S., et al.: Temporal neighbourhood aggregation: Predicting future links in temporal graphs via recurrent variational graph convolutions. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 5336–5345. IEEE (2019)
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Chen, J., et al.: E-LSTM-D: a deep learning framework for dynamic network link prediction. IEEE Trans. Syst. Man Cybern. Syst. 51(6), 3699–3712 (2019)
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)
Chen, Z., Ling, X., Feng, X., Zheng, H., Xu, Y.: Short-term traffic state prediction approach based on FCM and random forest. J. Electron. Inf. Technol. 40(8), 1879–1886 (2018)
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)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929 (2019)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hu, J., Guo, C., Yang, B., Jensen, C.S., Chen, L.: Recurrent multi-graph neural networks for travel cost prediction. arXiv preprint arXiv:1811.05157 (2018)
Ke, J., Qin, X., Yang, H., Zheng, Z., Zhu, Z., Ye, J.: Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. arXiv preprint arXiv:1910.09103 (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lei, K., Qin, M., Bai, B., Zhang, G., Yang, M.: GCN-GAN: a non-linear temporal link prediction model for weighted dynamic networks. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 388–396. IEEE (2019)
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. Transp. Res. Part C Emerg. Technol. 54, 187–197 (2015)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297, Oakland, CA, USA (1967)
Wu, Y., Tan, H.: Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv preprint arXiv:1612.01022 (2016)
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. arXiv preprint arXiv:1610.00081 (2016)
Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X.: DNN-based prediction model for spatio-temporal data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–4 (2016)
Zhang, J., Zheng, Y., Sun, J., Qi, D.: Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Comput. Archit. Lett. 32(03), 468–478 (2020)
Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction (2018)
Zhao-sheng, Y., Yuan, W., Qing, G.: Short-term traffic flow prediction method based on SVM. J. Jilin Univ. (Eng. Technol. Ed.) 6, 009 (2006)
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)
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Jiang, L. et al. (2021). Dynamic Transit Flow Graph Prediction in Spatial-Temporal Network. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13080. Springer, Cham. https://doi.org/10.1007/978-3-030-90888-1_46
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