Skip to main content
SpringerLink
Log in

An effective dynamic spatiotemporal framework with external features information for traffic prediction

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Traffic prediction is necessary for management departments to dispatch vehicles and for drivers to avoid congested roads. Many traffic forecasting methods based on deep learning have been proposed in recent years, and their main aim is to solve the problem of spatial dependencies and temporal dynamics. This paper proposes a useful dynamic model to predict the urban traffic volume by combining fully bidirectional LSTM, a complex attention mechanism, and external features, including weather conditions and events. First, we adopt bidirectional LSTM to obtain temporal dependencies of traffic volume dynamically in each layer, which is different from the hybrid methods combining bidirectional and unidirectional approaches. Second, we use a more elaborate attention mechanism to learn short-term and long-term periodic temporal dependencies. Finally, we collect weather condition and event information as external features to further improve the prediction precision. The experimental results show that the proposed model improves the prediction precision by approximately 3-7 percent on the NYC-Taxi and NYC-Bike datasets compared to the most recently developed method and is therefore a useful tool for urban traffic prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. https://darksky.net

References

  1. Belhadi A, Djenouri Y, Djenouri D, Lin JCW (2020) A recurrent neural network for urban long-term traffic flow forecasting. Appl Intell

  2. Cui Z, Henrickson K, Ke R, Wang Y (2018) Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. CoRR arXiv:1802.07007

  3. Cui Z, Ke R, Wang Y (2018) Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. CoRR arXiv:1801.02143, 1–12

  4. Greenberg H (1959) An analysis of traffic flow. Oper Res 7(1):79–85

    Article  MathSciNet  Google Scholar 

  5. Guo J, Williams BM (2010) Real-time short-term traffic speed level forecasting and uncertainty quantification using layered kalman filters. Transp Res Rec 2175(1):28–37

    Article  Google Scholar 

  6. 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

  7. Guo K, Hu Y, Qian Z, Liu H, Zhang K, Sun Y, Gao J, Yin B (2020) Optimized graph convolution recurrent neural network for traffic prediction. IEEE Trans Intell Transp Syst 1–12

  8. Jeong YS, Byon YJ, Castro-Neto MM, Easa SM (2013) Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 14(4):1700–1707

    Article  Google Scholar 

  9. Kenney JF, Keeping ES (1964) §4.15 Root mean square. In: Mathematics of statistics, Pt 1, 3rd edn. Van Nostrand, Princeton, pp 59–60

  10. LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  11. Luo X, Li D, Yang Y, Zhang S (2019) Spatiotemporal traffic flow prediction with KNN and LSTM. J Adv Transp 2019:10

    Google Scholar 

  12. Lv Y, Duan Y, Kang W, Li Z, Wang F (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

    Google Scholar 

  13. Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, pp 802–810

  14. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of go with deep neural networks and tree search. Nature 529:484–489

    Article  Google Scholar 

  15. Sun Y, Leng B, Guan W (2015) A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166:109–121

    Article  Google Scholar 

  16. Szeto W Y, Ghosh B, Basu B, O’Mahony M (2009) Multivariate traffic forecasting technique using cell transmission model and SARIMA model. J Transp Eng 135(9):658–667

    Article  Google Scholar 

  17. Tian C, Zhu X, Hu Z, Ma J (2020) Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attentionmechanism. Appl Intell

  18. Tong Y, Chen Y, Zhou Z, Chen L, Wang J, Yang Q, Ye J, Lv W (2017) The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1653–1662

  19. Van Lint J, Van Hinsbergen C (2012) Short-term traffic and travel time prediction models. Artif Intell Appl Critical Transp Issues 22(1):22–41

    Google Scholar 

  20. Wang D, Cao W, Li J, Ye J (2017) DeepSD: supply-demand prediction for online car-hailing services using deep neural networks. In: 2017 IEEE 33rd international conference on data engineering (ICDE). IEEE, pp 243–254

  21. Williams B M, 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 

  22. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30 (1):79–82. http://www.jstor.org/stable/24869236

    Article  Google Scholar 

  23. Wu Y, Tan H (2016) Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. CoRR arXiv:1612.01022, 1–14

  24. Xie Y, Zhang Y, Ye Z (2007) Short-term traffic volume forecasting using kalman filter with discrete wavelet decomposition. Comput-Aided Civil Infrastruct Eng 22(5):326–334

    Article  Google Scholar 

  25. Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), the 30th innovative applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2–7, 2018, pp 2588–2595

  26. Yao H, Tang X, Wei H, Zheng G, Li Z (2019) Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5668–5675

  27. Ye J, Sun L, Du B, Fu Y, Tong X, Xiong H (2019) Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, pp 305–313

  28. Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7):1501

    Article  Google Scholar 

  29. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, {IJCAI-18}. IJCAI, pp 3634–3640

  30. Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 1655–1661

  31. Zhang H, Wang X, Gao J, Tang M, Guo Y (2018) A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics. Appl Intell 48:2429–2440

    Article  Google Scholar 

  32. Zhang J, Zheng Y, Qi D, Li R, Yi X, Li T (2018) Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif Intell 259:147–166

    Article  MathSciNet  Google Scholar 

  33. 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. https://doi.org/10.1109/TITS.2019.2935152

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (NSFC 61572005, 61672086, 61702030, 61771058).

Author information

Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Jichen Wang, Weiguo Zhu, Yongqi Sun & Chunzi Tian

Authors
  1. Jichen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiguo Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongqi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chunzi Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongqi Sun.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Zhu, W., Sun, Y. et al. An effective dynamic spatiotemporal framework with external features information for traffic prediction. Appl Intell 51, 3159–3173 (2021). https://doi.org/10.1007/s10489-020-02043-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-02043-1

Keywords

Search

Navigation