International Conference on Transportation and Development 2020
Short-Term Traffic Flow Prediction Based on Graph Convolutional Network Embedded LSTM
Publication: International Conference on Transportation and Development 2020
ABSTRACT
Short-term traffic flow prediction, which is useful to improve traffic congestion and road efficiency, has been a hot issue in the field of transportation. However, only considering Euclidean space, conventional methods are always unable to make good use of the spatial-temporal correlation of traffic flow data which is usually a topological structure. In this paper, a deep learning model, GCN-LSTM (graph convolutional network-LSTM), was proposed with encoder and decoder structure. GCN-LSTM will simultaneously capture the spatial and temporal characteristic of traffic flow by embedding GCN into the structure of LSTM. Training with the traffic flow data of previous T moments and adjacent section, GCN-LSTM effectively perform short-term traffic flow prediction. Experiments on real data demonstrate that our method, considering both of spatial and temporal features, has a more powerful representation ability and higher prediction accuracy compared with LSTM.
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REFERENCES
Chen, C., Petty, K., Skabardonis, A., Varaiya, P., and Jia, Z. (2001). “Freeway performance measurement system: Mining loop detector data.” Transportation Research Record.
Chen, M., Yu, X., and Liu, Y. (2018). “PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction.” IEEE Transactions on Intelligent Transportation Systems.
Cui, Z., Henrickson, K., Ke, R., and Wang, Y. (2018). “Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.”
Dou, H., Yang, X., Wu, Z., and Liu, H. (2008). “A study of traffic flow prediction based on wavelet analysis and ARIMA.” Proceedings of the 8th International Conference of Chinese Logistics and Transportation Professionals – Logistics: The Emerging Frontiers of Transportation and Development in China, 4260–4266.
Fu, G., Han, G. Q., Lu, F., and Xu, Z. X. (2013). “Short-term traffic flow forecasting model based on support vector machine regression.” Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science), 41(9), 71–76.
Glorot, X., and Bengio, Y. (2010). “Understanding the difficulty of training deep feedforward neural networks.” Journal of Machine Learning Research, 9, 249–256.
Huang, W., Song, G., Hong, H., and Xie, K. (2014). “Deep architecture for traffic flow prediction: Deep belief networks with multitask learning.” IEEE Transactions on Intelligent Transportation Systems.
Lv, Y., Duan, Y., Kang, W., Li, Z., and Wang, F. Y. (2015). “Traffic Flow Prediction with Big Data: A Deep Learning Approach.” IEEE Transactions on Intelligent Transportation Systems.
Vlahogianni, E. I., Karlaftis, M. G., and Golias, J. C. (2014). “Short-term traffic forecasting: Where we are and where we’re going.” Transportation Research Part C: Emerging Technologies.
Wang, X., An, K., Tang, L., and Chen, X. (2015). “Short Term Prediction of Freeway Exiting Volume Based on SVM and KNN.” International Journal of Transportation Science and Technology.
Williams, B. M., and Hoel, L. A. (2003). “Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results.” Journal of Transportation Engineering.
Xie, Y., Zhang, Y., and Ye, Z. (2007). “Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition.” Computer-Aided Civil and Infrastructure Engineering.
Yu, B., Yin, H., and Zhu, Z. (2018). “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.” IJCAI International Joint Conference on Artificial Intelligence.
Zhang, J., Wang, F. Y., Wang, K., Lin, W. H., Xu, X., and Chen, C. (2011). “Data-driven intelligent transportation systems: A survey.” IEEE Transactions on Intelligent Transportation Systems.
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Published In
International Conference on Transportation and Development 2020
Pages: 159 - 168
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8315-2
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 31, 2020
Published in print: Aug 31, 2020
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