Abstract
Road traffic state prediction is a challenging task for urban traffic control and guidance due to the complicated spatial dependencies on the roadway network and the time-varying traffic flow data. In this work, a novel traffic flow prediction method named the graph convolutional recurrent neural network (GCRNN) is proposed to tackle this challenge. First, we address the problem on a graph and build the model with graph embedding techniques. Second, the proposed model employs the GCN model to learn the interactions of the roadways to capture the spatial dependence and uses the long short-term memory (LSTM) neural network (NN) to learn dynamic changes of traffic data to capture temporal dependence. An experiment is conducted on a Hangzhou transportation network with several typical intersections under the Sydney coordinated adaptive traffic system (SCATS), the results of which indicate that our model yields excellent performance in terms of different prediction error measures.
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References
Xu, D., Dong, H., Jia, L., Qin, Y.: Virtual speed sensors based algorithm for expressway traffic state estimation. Sci. Chin. Technol. Sci. 55(5), 1381–1390 (2012)
Xu, D.-w., Dong, H.-h., Jia, L.-m., Tian, Y.: Road traffic states estimation algorithm based on matching of regional traffic attracters. J. Central South Univ. 21(5), 2100–2107 (2014)
Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intel. Syst. Technol. 5(3), 38 (2014)
Xu, D., Dong, H., Li, H., Jia, L., Feng, Y.: The estimation of road traffic states based on compressive sensing. Transportmetrica B Trans. Dyn. 3(2), 131–152 (2015)
Hamed, M.M., Al-Masaeid, H.R., Bani Said, Z.M.: Short-term prediction of traffic volume in urban arterials. J. Transp. Eng. 121(3), 249–254 (1995)
Okutani, I., Stephanedes, Y.J.: Dynamic prediction of traffic volume through kalman filtering theory. Transp. Res. B Methodol. 18(1), 1–11 (1984)
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)
Guo, J., Huang, W., Williams, B.M.: Adaptive kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Trans. Res. C Emer. Technol. 43, 50–64 (2014)
Xu, D.-w., Wang, Y.-d., Jia, L.-m., Qin, Y., Dong, H.-h.: Real-time road traffic state prediction based on arima and kalman filter. Front. Inf. Technol. Elect. Eng. 18(2), 287–302 (2017)
Fusco, G., Colombaroni, C., Isaenko, N.: Short-term speed predictions exploiting big data on large urban road networks. Trans. Res. C Emer. Technol. 73, 183–201 (2016)
Zhang, Y., Zhang, Y., Haghani, A.: A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model. Trans. Res. C Emer. Technol. 43, 65–78 (2014)
Cai, P., Wang, Y., Lu, G., Chen, P., Ding, C., Sun, J.: A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Trans. Res. C Emer. Technol. 62, 21–34 (2016)
Xu, D., Wang, Y., Jia, L., Zhang, G., Guo, H.F.: Real-time road traffic states estimation based on kernel-KNN matching of road traffic spatial characteristics. Journal of Central South University 23(9), 2453–2464 (2016)
Xu, D., Wang, Y., Peng, P., Beilun, S., Deng, Z., Guo, H.: Real-time road traffic state prediction based on kernel-KNN. Transportmetrica A Trans. Sci. 1–15 (2018)
Lin, L., Li, Y., Sadek, A.: A k nearest neighbor based local linear wavelet neural network model for on-line short-term traffic volume prediction. Procedia. Soc. Behav. Sci. 96, 2066–2077 (2013)
Lin, L., Wang, Q., Sadek, A.W.: Short-term forecasting of traffic volume: evaluating models based on multiple data sets and data diagnosis measures. Transp. Res. Rec. 2392(1), 40–47 (2013)
Castro-Neto, M., Jeong, Y.-S., Jeong, M.-K., Han, L.D.: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Exp. Syst. Appl. 36(3), 6164–6173 (2009)
Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intel. Transp. Syst. 15(5), 2191–2201 (2014)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intel. Transp. Syst. 16(2), 865–873 (2015)
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)
Liu, Q., Wang, B., Zhu, Y.: Short-term traffic speed forecasting based on attention convolutional neural network for arterials. Comput. Aided Civ. Inf. Eng. 33(11), 999–1016 (2018)
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. Trans. Res. C Emer. Technol. 54, 187–197 (2015)
Van Lint, J.W.C., Hoogendoorn, S.P., van Zuylen, H.J.: Freeway travel time prediction with state-space neural networks: modeling state-space dynamics with recurrent neural networks. Transp. Res. Rec. 1811(1), 30–39 (2002)
Cui, Z., Ke, R., Wang, Y.: Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv preprint arXiv:1801.02143 (2018)
Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. Preprint. arXiv:1412.3555 (2014)
Wang, Y., Xu, D., Lu, Y., Shen, J., Zhang, G.: Compression algorithm of road traffic data in time series based on temporal correlation. IET Intel. Trans. Syst. 12(3), 177–185 (2018)
Xu, D.W., Wang, Y.D., Li, H.J., Qin, Y., Jia, L.M.: The measurement of road traffic states under high data loss rate. Measurement 69, 134–145 (2015)
Theocharidis, A., Van Dongen, S., Enright, A.J., Freeman, T.C.: Network visualization and analysis of gene expression data using biolayout express 3d. Nat. Prot. 4(10), 1535 (2009)
Freeman, L.: Visualizing social networks. Soc. Netw. Data Anal. 6(4), 411–429 (2000)
Cancho, R.F.I., Solé, R.V.: The small world of human language. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268(1482), 2261–2265 (2001)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. arXiv preprint arXiv:1606.09375 (2016)
Zhao, Z., Chen, W., Wu, X., Chen, P.C.Y., Liu, J.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intel. Trans. Syst. 11(2), 68–75 (2017)
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Xu, D., Dai, H., Xuan, Q. (2021). Graph Convolutional Recurrent Neural Networks: A Deep Learning Framework for Traffic Prediction. In: Xuan, Q., Ruan, Z., Min, Y. (eds) Graph Data Mining. Big Data Management. Springer, Singapore. https://doi.org/10.1007/978-981-16-2609-8_9
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DOI: https://doi.org/10.1007/978-981-16-2609-8_9
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