With traffic network becoming increasingly complicated, traffic flow prediction has important practical significance for the management of traffic roads and public safety. For example, an accurate taxi demand prediction can help to improve efficiency of vehicle scheduling and reduce traffic congestion. The main issue of flow prediction is how to extract the information of complex spatio-temporal dependencies and interactions between arrival and departure. To solve these problems, we develop a deep learning method based on time-dependent attention convolutional LSTM (TDAConvLSTM) in which a time-dependent attention mechanism is designed to learn similarities of historical traffic flows among different time intervals and a fusion mechanism is introduced to aggregate the feature information produced by convolutional LSTM and attention module. And then, the result of the feature aggregation is fed to a multi-layer deconvolutional network to gain the results of flow prediction. Experimental studies on two real-life datasets indicate that TDAConvLSTM achieves better results than the compared models. The source code of our proposed method is available at the URL1.
This is a preview of subscription content,to check access.
Access this article
Shi X, Chen Z, Wang H, Yeung D, Wong W, Woo W (2015) Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: Proceedings of the 28th international conference on neural information processing systems, vol 28, Cambridge, pp 802–810
Li J, Guo F, Sivakumar A, Dong Y, Krishnan R (2021) Transferability improvement in short-term traffic prediction using stacked LSTM network. Transp Res Part C Emerging Technol 124:102977
Guo S, Lin Y, Wan H, Li X, Cong G (2021) Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2021.3056502
Peng H, Du B, Liu M, Liu M, Ji S, Wang S, Zhang X, He L (2021) Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Inf Sci 578:401–416
Liu J, Guan W (2004) A summary of traffic flow forecasting methods. J Highway Transp Res Dev 21(3):82–85
Chandra S R, Al-Deek H (2009) Predictions of freeway traffic speeds and volumes using vector autoregressive models. J Intell Transp Syst 13(2):53–72
Chen C, Hu J, Meng Q, Zhang Y (2011) Short-time traffic flow prediction with arima-garch model. In: Proceedings of IEEE intelligent vehicles symposium, pp 607–612
Kamarianakis Y, Prastacos P (2003) Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transp Res Rec 1857(1):74–84
Klepsch J, Klüppelberg C, Wei T (2017) Prediction of functional arma processes with an application to traffic data. Econometr Stat 1:128–149
Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Li Z, Ye J (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the 32nd AAAI conference on artificial intelligence, vol 32, pp 2588–2595
Fang S, Zhang Q, Meng G, Xiang S, Pan C (2019) Gstnet: global spatial-temporal network for traffic flow prediction. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 2286–2293
Zheng C, Fan X, Wen C, Chen L, Wang C, Li J (2020) DeepSTD: Mining spatio-temporal disturbances of multiple context factors for citywide traffic flow prediction. IEEE Trans Intell Transp Syst 21(9):3744–3755
Yang B, Kang Y, Li H, Zhang Y, Yang Y, Zhang L (2020) Spatio-temporal expand-and-squeeze networks for crowd flow prediction in metropolis. IET Intell Transp Syst 14(5):313–322
Zhang J, Zheng Y, Sun J, Qi D (2019) Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Trans Knowl Data Eng 32(3):468–478
Liu Y, Liu Z, Jia R (2019) Deeppf: A deep learning based architecture for metro passenger flow prediction. Transp Res Part C: Emerging Technol 101:18–34
Zheng Z, Yang Y, Liu J, Dai H-N, Zhang Y (2019) Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Trans Intell Transp Syst 20(10):3927–3939
Du B, Peng H, Wang S, Bhuiyan M Z A, Wang L, Gong Q, Liu L, Li J (2020) Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans Intell Transp Syst 21(3):972–985
Peng H, Wang H, Du B, Bhuiyan M Z A, Ma H, Liu J, Wang L, Yang Z, Du L, Wang S et al (2020) Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Inf Sci 521:277–290
Lv M, Hong Z, Chen L, Chen T, Zhu T, Ji S (2021) Temporal multi-graph convolutional network for traffic flow prediction. IEEE Trans Intell Transp Syst 22(6):3337–3348
Qiu H, Zheng Q, Msahli M, Memmi G, Qiu M, Lu J (2020) Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans Intell Transp Syst 22:4560–4569
Huang R, Huang C, Liu Y, Dai G, Kong W (2020) LSGCN: Long short-term traffic prediction with graph convolutional networks.. In: Proceedings of the 29th international joint conference on artificial intelligence , pp 2355–2361
Du B, Hu X, Sun L, Liu J, Qiao Y, Lv W (2020) Traffic demand prediction based on dynamic transition convolutional neural network. IEEE Trans Intell Transp Syst 22(2):1237–1247
Xia T, Lin J, Li Y, Feng J, Hui P, Sun F, Guo D, Jin D (2021) 3dgcn: 3-dimensional dynamic graph convolutional network for citywide crowd flow prediction. ACM Trans Knowl Discov Data (TKDD) 15(6):1–21
Hao S, Lee D-H, Zhao D (2019) Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system. Transp Res Part C: Emerging Technol 107:287–300
Liu L, Zhen J, Li G, Zhan G, He Z, Du B, Lin L (2021) Dynamic spatial-temporal representation learning for traffic flow prediction. IEEE Trans Intell Transp Syst 22(11):7169–7183
Wang Z, Su X, Ding Z (2021) Long-term traffic prediction based on lstm encoder-decoder architecture. IEEE Trans Intell Transp Syst 22(10):6561–6571
Zheng C, Fan X, Wang C, Qi J (2020) Gman: A graph multi-attention network for traffic prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 1234–1241
Do Loan NN, Vu H L, Vo B Q, Liu Z, Phung D (2019) An effective spatial-temporal attention based neural network for traffic flow prediction. Transp Res Part C: Emerging Technol 108:12–28
Guo S, Lin Y, Feng N, Song C, Wan H (2019) 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
Zhang J, Zheng Y, Qi D (2017Feb.) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 31
Zhou X, Shen Y, Zhu Y, Huang L (2018) Predicting multi-step citywide passenger demands using attention-based neural networks. In: Proceedings of the eleventh ACM international conference on web search and data mining, New York, pp 736–744
This research was supported by the National Natural Science Foundation of China under Grant Nos.62062033 and 62067002, and the Natural Science Foundation of Jiangxi Province under Grant Nos.20212BAB202008 and 20192ACBL21006.
Conflict of Interests
All authors declare that No conflict of interest exists.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Huang, X., Tang, J., Yang, X. et al. A time-dependent attention convolutional LSTM method for traffic flow prediction. Appl Intell 52, 17371–17386 (2022). https://doi.org/10.1007/s10489-022-03324-7