HetGAT: a heterogeneous graph attention network for freeway traffic speed prediction

Abstract

As an essential part of the modern intelligent traffic management system, traffic speed prediction is a challenging task. In recent studies, deep neural networks (LSTM and WaveNet) and graph neural networks (GCN and GNN) have been extensively investigated on traffic networks evaluation, which is better than statistical-based models (MA and ARIMA). However, the demerits existing in these deep learning forecasting process include (1) carry out vehicle speed as an individual input and insufficient ability to handle the other related factors, such as the number of equivalent lanes, accident occurrence, and toll data; (2) inadequate capability of considering both linear and nonlinear components as a whole; (3) unstable performance on forecasting task given various heterogeneous series. Therefore, we propose a hybrid end-to-end model to combine both spatio-temporal features and other effective features. First, a heterogeneous graph attention network approach (HetGAT) was proposed, and a temporal dilated convolution architecture (TCN) was adopted to simulate the impacts on traffic flow of the multi-scale context of temporal factors. Then, a weighted graph attention network (GAT) encodes input temporal features, and a decoder predicts the output speed sequence via a freeway network structure. Based on the end-to-end architecture, we integrate multiple Spatio-temporal factors effectively for the prediction. To validate the efficiency of the proposed model, three sets of field-captured data were employed to run the test. Compared with conventional sequence analysis models and deep prediction models, experimental results demonstrated the superiority of HetGAT for all cases with regards to MAE, MAPE, and RMSE.

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References

  1. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR abs/1803.01271, arxiv:1803.01271

  2. Chatfield C (1978) The holt-winters forecasting procedure. J R Stat Soc: Ser C (Appl Stat) 27(3):264–279. https://doi.org/10.2307/2347162

    Article  Google Scholar 

  3. Chen J, Xu X, Wu Y, Zheng H (2018) Gc-lstm: Graph convolution embedded lstm for dynamic link prediction. arXiv preprint arXiv:181204206

  4. Chim S, Lee JG, Park HH (2019) Dilated skip convolution for facial landmark detection. Sensors 19(24):5350

    Article  Google Scholar 

  5. Cui Z, Ke R, Pu Z, Wang Y (2018) Deep bidirectional and unidirectional lstm recurrent neural network for network-wide traffic speed prediction. arxiv:1801.02143

  6. Feng H, Shu Y, Wang S, Ma M (2006) Svm-based models for predicting WLAN traffic. In: 2006 IEEE international conference on communications, vol 2, pp 597–602

  7. Frasconi P, Gori M, Sperduti A (1998) A general framework for adaptive processing of data structures. IEEE Trans Neural Netw 9(5):768–786

    Article  Google Scholar 

  8. Fu R, Zhang Z, Li L (2016) 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

  9. Geng X, Li Y, Wang L, Zhang L, Yang Q, Ye J, Liu Y (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. Proc AAAI Conf Artif Intell 33:3656–3663. https://doi.org/10.1609/aaai.v33i01.33013656

    Article  Google Scholar 

  10. Gers F (1999) Learning to forget: continual prediction with lstm. In: IET conference proceedings, pp 850–855(5)

  11. Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings. 2005 IEEE international joint conference on neural networks, IEEE, vol 2, pp 729–734

  12. Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc AAAI Conf Artif Intell 33:922–929. https://doi.org/10.1609/aaai.v33i01.3301922

    Article  Google Scholar 

  13. Hansun S (2013) A new approach of moving average method in time series analysis. In: 2013 Conference on new media studies (CoNMedia), pp 1–4

  14. Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191–2201

    Article  Google Scholar 

  15. Jagadish H, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C (2014) Big data and its technical challenges. Commun ACM 57(7):86,88–94

  16. Koesdwiady A, Soua R, Karray F (2016) Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans Veh Technol 65(12):9508–9517

    Article  Google Scholar 

  17. Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:170701926

  18. Liu RW, Chen J, Liu Z, Li Y, Liu Y, Liu J (2017) Vessel traffic flow separation-prediction using low-rank and sparse decomposition. In: Proceedings of ITSC, IEEE, pp 1–6

  19. Lowd D, Domingos P (2005) Naive bayes models for probability estimation. In: Proceedings of the 22nd international conference on machine learning, association for computing machinery, New York, NY, USA, ICML ’05, pp 529–536

  20. Ma X, Dai Z, He Z, Ma J, Wang Y, Wang Y (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4):818

    Article  Google Scholar 

  21. Ma X, Gao H, Xu H, Bian M (2019) An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing. EURASIP J Wirel Commun Netwo 2019(1):249

    Article  Google Scholar 

  22. Park C, Lee C, Bahng H, Kim K, Jin S, Ko S, Choo J et al (2019) Stgrat: a spatio-temporal graph attention network for traffic forecasting. arXiv preprint arXiv:191113181

  23. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Networks 20(1):61–80

    Article  Google Scholar 

  24. Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:171010903

  25. Vinayakumar R, Soman KP, Poornachandran P (2017) Applying deep learning approaches for network traffic prediction. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp 2353–2358

  26. Wang B, Zhang X, Zhou X, Li J (2020) A gated dilated convolution with attention model for clinical cloze-style reading comprehension. Int J Environ Res Public Health 17(4):1323

    Article  Google Scholar 

  27. Williams BM, Hoel LA (2003) Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J Transp Eng 129(6):664–672

    Article  Google Scholar 

  28. Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:151107122

  29. Yu F, Koltun V, Funkhouser T (2017) Dilated residual networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 472–480

  30. Zhang G (2003) Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50:159–175

    Article  Google Scholar 

  31. Zhang L, Liu Q, Yang W, Wei N, Dong D (2013) An improved k-nearest neighbor model for short-term traffic flow prediction. Procedia—Social and Behavioral Sciences 96:653–662, intelligent and integrated sustainable multimodal transportation systems proceedings from the 13th COTA international conference of transportation professionals (CICTP2013)

  32. Zhang X, You J (2020) A gated dilated causal convolution based encoder-decoder for network traffic forecasting. IEEE Access 8:6087–6097

    Article  Google Scholar 

  33. Zhao L, Song Y, Deng M, Li H (2018) Temporal graph convolutional network for urban traffic flow prediction method. arXiv:1811.05320

  34. Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2019) T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848–3858. https://doi.org/10.1109/TITS.2019.2935152

    Article  Google Scholar 

  35. Zheng C, Fan X, Wang C, Qi J (2019) Gman: A graph multi-attention network for traffic prediction. arxiv:1911.08415

Download references

Acknowledgements

This research was supported by research project of Zhejiang Communication Investment Group Co. LTD. (No. 201902), Zhejiang Department of Transportation (No. 2019006) and the Natural Science Foundation of Zhejiang Province of China under Grant (No. LY21F020003).

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Correspondence to Tao Ruan.

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Jin, C., Ruan, T., Wu, D. et al. HetGAT: a heterogeneous graph attention network for freeway traffic speed prediction. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-020-02807-0

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Keywords

  • Traffic speed prediction
  • Multiple factors
  • Dilated convolution network
  • Heterogeneous graph attention network