Skip to main content

Advertisement

ST-NAMN: a spatial-temporal nonlinear auto-regressive multichannel neural network for traffic prediction

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Accurate and efficient traffic information prediction is significantly important for the management of intelligent transportation systems. The traffic status (e.g., speed or flow) on one road segment is spatially affected by both its nearby neighbors and distant locations. The impending traffic status can be temporally influenced not only by its recent status but also by the randomness of its historical status change. The current state-of-the-art methods have effectively captured the spatio-temporal dependencies of road networks. However, most existing methods overlook the impact of time delay when capturing dynamic time dependencies. In addition, aggregating roads with similar traffic patterns from a wide range of spatial associations still poses challenges. In this paper, a spatial-temporal nonlinear auto-regressive multi-channel neural network (ST-NAMN) model is proposed to reveal the sophisticated nonlinear dynamic interconnections between temporal and spatial dependencies in road traffic data. Considering the temporal periodicity and spatial pattern similarity inherently in road traffic data, a divided period latent similarity correlation matrix (DLSC) first is utilized to calculate the similarity of traffic patterns from historical observation data. Then, we introduce an output feedback to the multi-layer perceptron (MLP) through a delay unit, which enables the output-layer to feedback its result data to the input layer in real-time, and further participate in the next iterative training to boost the learning capacity. Furthermore, an Enhanced-Bayesian Regularization weight updating method (EBR) is designed to better contemplate the influence of the continuous and delayed observation points compared to existing optimizers during the learning procedure. Experimental tests have been carried out on four real-world datasets and the results demonstrated that the proposed ST-NAMN method outperforms other state-of-the-art models.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Algorithm 1
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

Data used in the paper is publicly available at Caltrans Performance Measurement System http://pems.dot.ca.gov/ and Zenodo https://zenodo.org/record/1205229.

References

  1. Yin X, Wu G, Wei J, Shen Y, Qi H, Yin B (2022) Deep learning on traffic prediction: methods, analysis, and future directions. IEEE Trans Intell Transp Syst 23(6):4927–4943. https://doi.org/10.1109/TITS.2021.3054840

    Article  MATH  Google Scholar 

  2. Tedjopurnomo DA, Bao Z, Zheng B, Choudhury FM, Qin AK (2022) A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Trans Knowl Data Eng 34(4):1544–1561. https://doi.org/10.1109/TKDE.2020.3001195

    Article  Google Scholar 

  3. Zhu L, Chen C, Wang H, Yu FR, Tang T (2024) Machine learning in urban rail transit systems: a survey. IEEE Trans Intell Transp Syst 25(3):2182–2207. https://doi.org/10.1109/TITS.2023.3319135

    Article  MATH  Google Scholar 

  4. Yang H, Yu W, Zhang G, Du L (2024) Network-wide traffic flow dynamics prediction leveraging macroscopic traffic flow model and deep neural networks. IEEE Trans Intell Transp Syst 25(5):4443–4457. https://doi.org/10.1109/TITS.2023.3329489

    Article  MATH  Google Scholar 

  5. Aljebreen M et al (2024) Enhancing traffic flow prediction in intelligent cyber-physical systems: a novel bi-LSTM-based approach with kalman filter integration. IEEE Trans Consum Electron 70(1):1889–1902. https://doi.org/10.1109/TCE.2023.3335155

    Article  MATH  Google Scholar 

  6. Ding C, Duan J, Zhang Y, Wu X, Yu G (2018) Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility. IEEE Trans Intell Transp Syst 19(4):1054–1064. https://doi.org/10.1109/TITS.2017.2711046

    Article  MATH  Google Scholar 

  7. Liu C, Hoi SCH, Zhao P, Sun J (2016) Online ARIMA algorithms for time series prediction. In: 30th AAAI conference on artificial intelligence, pp 1867–1873

  8. Li C, Xu P (2021) Application on traffic flow prediction of machine learning in intelligent transportation. Neural Comput Appl 33(2):613–624

    Article  MathSciNet  MATH  Google Scholar 

  9. Han L, Zheng K, Zhao L, Wang X, Shen X (2019) Short-term traffic prediction based on deepcluster in large-scale road networks. IEEE Trans Veh Technol 68(12):12301–12313. https://doi.org/10.1109/TVT.2019.2947080

    Article  MATH  Google Scholar 

  10. Deng D, Shahabi C, Demiryurek U et al (2016) Latent space model for road networks to predict time-varying traffic. In: 22nd ACM SIGKDD international conference on knowledge discovery and data mining (KDD’16). New York, USA, pp 1525–1534

  11. Baggag A et al (2021) Learning spatiotemporal latent factors of traffic via regularized tensor factorization: imputing missing values and forecasting. IEEE Trans Knowl Data Eng 33(6):2573–2587. https://doi.org/10.1109/TKDE.2019.2954868

    Article  MATH  Google Scholar 

  12. Chen X, Sun L (2022) Bayesian temporal factorization for multidimensional time series prediction. IEEE Trans Pattern Anal Mach Intell 44(9):4659–4673. https://doi.org/10.1109/TPAMI.2021.3066551

    Article  MATH  Google Scholar 

  13. Zhao F, Zeng G-Q, Lu K-D (2020) EnLSTM-WPEO: short-term traffic flow prediction by ensemble LSTM, NNCT weight integration, and population extremal optimization. IEEE Trans Veh Technol 69(1):101–113. https://doi.org/10.1109/TVT.2019.2952605

    Article  MATH  Google Scholar 

  14. Ting P-Y et al (2020) Freeway travel time prediction using deep hybrid model - taking Sun Yat-Sen freeway as an example. IEEE Trans Veh Technol 69(8):8257–8266. https://doi.org/10.1109/TVT.2020.2999358

    Article  MATH  Google Scholar 

  15. Lee EH (2023) Traffic speed prediction of urban road network based on high importance links using XGB and SHAP. IEEE Access 11:113217–113226. https://doi.org/10.1109/ACCESS.2023.3324035

    Article  MATH  Google Scholar 

  16. Zhao D, Chen F (2022) A hybrid ensemble model for urban lane-level traffic flow prediction. IEEE J Radio Freq Identif 6:820–824. https://doi.org/10.1109/JRFID.2022.3217031

    Article  MATH  Google Scholar 

  17. Shu W, Cai K, Xiong NN (2022) A short-term traffic flow prediction model based on an improved gate recurrent unit neural network. IEEE Trans Intell Transp Syst 23(9):16654–16665. https://doi.org/10.1109/TITS.2021.3094659

    Article  Google Scholar 

  18. Fouladgar M, Parchami M, Elmasri R, Ghaderi A (2017) Scalable deep traffic flow neural networks for urban traffic congestion prediction. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, pp 2251–2258. https://doi.org/10.1109/IJCNN.2017.7966128.

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

  20. Zhang J, Zheng Y, Qi D et al (2018) Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif Intell 259:147–166

    Article  MathSciNet  MATH  Google Scholar 

  21. Guo S, Lin Y, Li S, Chen Z, Wan H (2019) Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst 20(10):3913–3926. https://doi.org/10.1109/TITS.2019.2906365

    Article  MATH  Google Scholar 

  22. Sun X, Wang X, Huang B et al (2023) Multidirectional short-term traffic volume prediction based on spatiotemporal networks. Appl Intell 53:24458–24473. https://doi.org/10.1007/s10489-023-04792-1

    Article  MATH  Google Scholar 

  23. Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C Emerg Technol 54:187–197

    Article  MATH  Google Scholar 

  24. Ma C, Dai G, Zhou J (2022) Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method. IEEE Trans Intell Transp Syst 23(6):5615–5624. https://doi.org/10.1109/TITS.2021.3055258

    Article  MATH  Google Scholar 

  25. Chen Y, Guo J, Xu H, Huang J, Su L (2023) Improved long short-term memory-based periodic traffic volume prediction method. IEEE Access 11:103502–103510. https://doi.org/10.1109/ACCESS.2023.3305398

    Article  Google Scholar 

  26. Ma C, Zhao Y, Dai G, Xu X, Wong S-C (2023) A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction. IEEE Trans Intell Transp Syst 24(4):3728–3737. https://doi.org/10.1109/TITS.2021.3117835

    Article  MATH  Google Scholar 

  27. Hu H, Lin Z, Hu Q, Zhang Y (2022) Attention mechanism with spatial-temporal joint model for traffic flow speed prediction. IEEE Trans Intell Transp Syst 23(9):16612–16621. https://doi.org/10.1109/TITS.2021.3113935

    Article  MATH  Google Scholar 

  28. Ma X, Zheng B, Jiang G, Liu L (2023) Cellular network traffic prediction based on correlation ConvLSTM and self-attention network. IEEE Commun Lett 27(7):1909–1912. https://doi.org/10.1109/LCOMM.2023.3275327

    Article  MATH  Google Scholar 

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

  30. Rahmani S, Baghbani A, Bouguila N, Patterson Z (2023) Graph neural networks for intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 24(8):8846–8885. https://doi.org/10.1109/TITS.2023.3257759

    Article  MATH  Google Scholar 

  31. Gupta A, Maurya MK, Goyal N et al (2023) ISTGCN: integrated spatio-temporal modeling for traffic prediction using traffic graph convolution network. Appl Intell 53:29153–29168. https://doi.org/10.1007/s10489-023-04976-9

    Article  MATH  Google Scholar 

  32. Su Z, Liu T, Hao X et al (2023) Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters. J Supercomput 79:18293–18312. https://doi.org/10.1007/s11227-023-05383-0

    Article  MATH  Google Scholar 

  33. Zhao L et al (2020) 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  MATH  Google Scholar 

  34. Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: 6th International conference on learning representations, ICLR

  35. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: IJCAI International joint conference on artificial intelligence, pp 3634–3640

  36. Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. In: IJCAI International joint conference on artificial intelligence, pp 1907–1913

  37. Guo S, Lin Y, Wan H, Li X, Cong G (2022) Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans Knowl Data Eng 34(11):5415–5428. https://doi.org/10.1109/TKDE.2021.3056502

    Article  MATH  Google Scholar 

  38. Wang X, Ma Y, Wang Y et al (2020) Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the world wide web conference, WWW, pp 1082–1092

  39. 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, pp 1234–1241

  40. Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C (2020) Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (KDD’20), pp 753–763

  41. Oreshkin BN, Amini A, Coyle L, Coates MJ (2021) FC-GAGA: fully connected gated graph architecture for spatio-temporal traffic forecasting. In: 35th AAAI conference on artificial intelligence

  42. Chen X, Chen Y, He Z (2018) Urban traffic speed dataset of Guangzhou, Sun Yat-Sen University, China, Zenodo. https://zenodo.org/record/1205229

  43. Han L, Du B, Sun L, Fu Y, Lv Y, Xiong H (2021) Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining (KDD’21), pp 547–555. https://doi.org/10.1145/3447548.3467275

  44. Liu H, Dong Z, Jiang R, Deng J, Deng J, Chen Q, Song X (2023) Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting. In: Proceedings of ACM international conference on information and knowledge management (CIKM), pp 4125–4129

  45. Ren Q, Li Y, Liu Y (2023) Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting. Expert Syst Appl 227:120203

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grant 2022YFB4501704.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to this paper. Data processing, methodology, algorithm design, and experiments were performed by Jiankai Zuo. The first draft of the manuscript was written by Jiankai Zuo. Analysis of experimental results, revision, and improvement of the first draft were finished by Yaying Zhang.

Corresponding author

Correspondence to Yaying Zhang.

Ethics declarations

Conflict of Interest

Authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zuo, J., Zhang, Y. ST-NAMN: a spatial-temporal nonlinear auto-regressive multichannel neural network for traffic prediction. Appl Intell 55, 14 (2025). https://doi.org/10.1007/s10489-024-06055-z

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-06055-z

Keywords