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RSAB-ConvGRU: A hybrid deep-learning method for traffic flow prediction

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Abstract

Accurate and real-time traffic flow prediction is crucial in intelligent transportation systems (ITS), and the traditional shallow prediction methods are challenging to capture the nonlinearity and uncertainty of traffic data effectively. To this end, this paper proposes a hybrid deep-learning method based on Residual Self-Attention and Bidirectional Gated Recurrent Unit combined with a Convolution-Gated Recurrent Unit (RSAB-ConvGRU) network to improve the accuracy of traffic flow prediction. The method consists of an RSA-ConvGRU module and two Bidirectional GRU (Bi-GRU) modules. The RSA-ConvGRU module includes a convolution-gated recurrent unit (Conv-GRU) module and a residual self-attention mechanism (RSA) module. Specifically, the Conv-GRU utilizes the convolutional and gated recurrent unit to extract spatial and temporal features. Moreover, the residual self-attention mechanism is used to determine the contribution of traffic features at different periods and stabilize the network’s training process to improve Conv-GRU’s prediction performance. Finally, the Bi-GRU module obtains the periodic characteristics and forward and backward variance trends in traffic flow data. The experimental results show that the accuracy of the RSAB-ConvGRU method is superior to state-of-the-art methods, such as SVR, LSTM, GRU, DCRNN, CNN-GRU-Attention, Conv-LSTM, AT-Conv-LSTM, Stacked-LSTM, and LSTM-RNN with Attention. Compared to the above nine methods, with a prediction time of 60 minutes and the urban traffic data, the MAPE values of RSAB-ConvGRU are reduced by 56.01%, 22.97%, 25.64%, 16.55%, 7.57%, 11.11%, 12.58%, 7.64%, and 6.3%, respectively.

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Data Availibility Statement

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Awad M, Khanna R (2015) Support vector regression, in: Efficient Learning Machines, Springer, pp 67–80

  2. Belhadi A, Djenouri Y, Djenouri D, Lin JCW (2020) A recurrent neural network for urban long-term traffic flow forecasting. Appl Intell 50:3252–3265

    Google Scholar 

  3. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5:157–166

    CAS  PubMed  Google Scholar 

  4. Cai L, Zhang Z, Yang J, Yu Y, Zhou T, Qin J (2019) A noise-immune Kalman filter for short-term traffic flow forecasting. Physica A: Statistical Mechanics and Its Applications 536:122601

    Google Scholar 

  5. Chen X, Wu S, Shi C, Huang Y, Yang Y, Ke R, Zhao J (2020) Sensing data supported traffic flow prediction via denoising schemes and ANN: a comparison. IEEE Sensors J 20:14317–14328

    ADS  Google Scholar 

  6. Cho K, van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN Encoder–Decoder for statistical machine translation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Doha, Qatar pp 1724–1734

  7. Djenouri Y, Belhadi A, Lin JCW, Cano A (2019) Adapted k-nearest neighbors for detecting anomalies on spatio-temporal traffic flow. IEEE Access 7:10015–10027

    Google Scholar 

  8. Djenouri Y, Belhadi A, Chen HC, Lin JCW (2022) Intelligent deep fusion network for urban traffic flow anomaly identification. Comput Commun 189:175–181

    Google Scholar 

  9. Djenouri Y, Belhadi A, Srivastava G, Lin JCW (2023) Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Futur Gener Comput Syst 139:100–108

    Google Scholar 

  10. Djenouri Y, Belhadi A, Lin JCW, Djenouri D, Cano A (2019) A survey on urban traffic anomalies detection algorithms, IEEE Access vol 7 p 12192–12205

  11. Du S, Li T, Gong X, Horng SJ (2020) A hybrid method for traffic flow forecasting using multimodal deep learning. Int J Comput Intell Syst 13:85–97

    Google Scholar 

  12. Duan Y, Kang W, Li Z, Wang FY (2014) Traffic flow prediction with big data: A deep learning approach. IEEE Trans on J Intell Transp Syst 16:865–873

    Google Scholar 

  13. Emami A, Sarvi M, Asadi Bagloee S (2019) Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment. Journal of Modern Transportation 27:222–232

    Google Scholar 

  14. Fu R, Zhang Z, Li L (2016) Using LSTM and GRU neural network methods for traffic flow prediction, in: 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, p 324–328

  15. Ghanim MS, Muley D, Kharbeche M (2022) ANN-based traffic volume prediction models in response to COVID-19 imposed measures. Sustainable Cities and Society 81:103830

    PubMed  PubMed Central  Google Scholar 

  16. Giraka O, Selvaraj VK (2020) Short-term prediction of intersection turning volume using seasonal ARIMA model. Transportation Letters 12:483–490

    Google Scholar 

  17. Gu Y, Lu W, Qin L, Li M, Shao Z (2019) Short-term prediction of lane-level traffic speeds: A fusion deep learning model. Transp Res C: Emerg Technol 106:1–16

    Google Scholar 

  18. Habtemichael FG, Cetin M (2016) Short-term traffic flow rate forecasting based on identifying similar traffic patterns. Transportation Research Part C: Emerging Technologies 66:61–78

    Google Scholar 

  19. Hu H, Lin Z, Hu Q, Zhang Y (2021) Attention mechanism with spatial-temporal joint model for traffic flow speed prediction. IEEE Trans Intell Transp Syst. 23:16612–16621

    Google Scholar 

  20. Hu X, Liu T, Hao X, Lin C (2022) Attention-based Conv-LSTM and Bi-LSTM networks for large-scale traffic speed prediction, J Supercomput p 1–24

  21. Khajeh Hosseini M, Talebpour A (2019) Traffic prediction using time-space diagram: A convolutional neural network approach. Trans Res Rec 2673:425–435

    Google Scholar 

  22. Kolen JF, Kremer SC (2001) Gradient flow in recurrent nets: The difficulty of learning longterm dependencies, A Field Guide to Dynamical Recurrent Networks p 237–243

  23. Kumar PB, Hariharan K et al (2022) Time series traffic flow prediction with hyper-parameter optimized arima models for intelligent transportation system, Journal of Scientific and Industrial Research 81 p 408–415

  24. Lin X, Huang Y (2021) Short-term high-speed traffic flow prediction based on ARIMA-GARCH-M model. Wirel Pers Commun 117:3421–3430

    Google Scholar 

  25. Liu Y, Zheng H, Feng X, Chen Z (2017) Short-term traffic flow prediction with Conv-LSTM, in: 9th International Conference on Wireless Communications and Signal Processing (WCSP), IEEE, pp 1–6

  26. Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting, Int Conf Learn Represent 00 (ICLR)

  27. Luo J (2020) Short-term traffic flow prediction method in bayesian networks based on quantile regression. Promet-Traffic & Transportation 32:821–835

    Google Scholar 

  28. Meng M, Toan TD, Wong YD, Lam SH (2022) Short-term travel-time prediction using support vector machine and nearest neighbor method. Transp Res Rec, p 03611981221074371

  29. Mondal MA, Rehena Z (2022) Stacked lstm for short-term traffic flow prediction using multivariate time series dataset. Arab J Sci Eng 47:10515–10529

    Google Scholar 

  30. Moniruzzaman M, Maoh H, Anderson W (2016) Short-term prediction of border crossing time and traffic volume for commercial trucks: A case study for the Ambassador Bridge. Transp Res C: Emerging Technologies 63:182–194

    Google Scholar 

  31. Nagaraj N, Gururaj HL, Swathi BH, Hu YC (2022) Passenger flow prediction in bus transportation system using deep learning. Multimedia Tools and Applications 81:12519–12542

    PubMed  PubMed Central  Google Scholar 

  32. Nie L, Jiang D, Yu S, Song H (2017) Network traffic prediction based on deep belief network in wireless mesh backbone networks, in: 2017 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, p 1–5

  33. Poonia P, Jain V, Short-term traffic flow prediction: using LSTM, in: 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), IEEE, pp 1–4

  34. Priambodo B, Ahmad A, Kadir RA (2021) Spatio-temporal K-NN prediction of traffic state based on statistical features in neighbouring roads. J Intell & Fuzzy Syst 40:9059–9072

    Google Scholar 

  35. Ren C, Chai C, Yin C, Ji H, Cheng X, Gao G, Zhang H (2021) Short-term traffic flow prediction: A method of combined deep learnings. J Adv Transp 2021:1–15

    CAS  Google Scholar 

  36. Sha S, Li J, Zhang K, Yang Z, Wei Z, Li X, Zhu X (2020) RNN-based subway passenger flow rolling prediction. IEEE Access 8:15232–15240

    Google Scholar 

  37. Shu W, Cai K, Xiong NN (2022) A short-term traffic flow prediction model based on an improved gate recurrent unit neural network. IEEE Transactions on Intelligent Transportation Systems 23:16654–16665

    Google Scholar 

  38. Shu W, Cai K, Xiong NN, (2021) A short-term traffic flow prediction model based on an improved gate recurrent unit neural network. IEEE Trans Intell Transp Syst

  39. Sun B, Cheng W, Goswami P, Bai G (2018) Short-term traffic forecasting using self-adjusting k-nearest neighbours. IET Intell Transp Syst 12:41–48

    Google Scholar 

  40. Thaduri A, Polepally V, Vodithala S (2021) Traffic Accident Prediction based on CNN Model, 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) p 1590–1594

  41. Toan TD, Truong VH (2021) Support vector machine for short-term traffic flow prediction and improvement of its model training using nearest neighbor approach. Transp Res Rec 2675:362–373

    Google Scholar 

  42. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need, Advances in Neural Information Processing Systems vol 30

  43. Wang J, Deng W, Guo Y (2014) New Bayesian combination method for short-term traffic flow forecasting. Transp Res C: Emerg Technol 43:79–94

    CAS  Google Scholar 

  44. Wu Y, Tan H, Qin L, Ran B, Jiang Z (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res C: Emerg Technol 90:166–180

    Google Scholar 

  45. Xia D, Wang B, Li H, Li Y, Zhang Z (2016) A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting. Neurocomputing 179:246–263

    Google Scholar 

  46. Xia D, Zhang M, Yan X, Bai Y, Zheng Y, Li Y, Li H (2021) A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction. Neural Comput & Applic 33:2393–2410

    Google Scholar 

  47. Xia D, Yang N, Jiang S, Hu Y, Li Y, Li H, Wang L (2022) A parallel NAW-DBLSTM algorithm on Spark for traffic flow forecasting. Neural Computing and Applications 34:1557–1575

    Google Scholar 

  48. Xia D, Yang N, Jian S, Hu Y, Li H (2022) SW-BiLSTM: a Spark-based weighted BiLSTM model for traffic flow forecasting, Multimedia Tools and Applications p 1–26

  49. Xie DF, Fang ZZ, Jia B, He Z (2019) A data-driven lane-changing model based on deep learning. Transp Res C: Emerg Technol 106:41–60

    Google Scholar 

  50. Xu H, Jiang C (2020) Deep belief network-based support vector regression method for traffic flow forecasting. Neural Comput & Applic 32:2027–2036

    Google Scholar 

  51. Xu X, Jin X, Xiao D, Ma C, Wong SC (2021) A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction. J Intell Transp Syst vol 0 p 1–18

  52. Yang D, Li S, Peng Z, Wang P, Wang J, Yang H (2019) MF-CNN: traffic flow prediction using convolutional neural network and multi-features fusion. IEICE Trans Inform Syst 102:1526–1536

    ADS  Google Scholar 

  53. Yu C, Lam KC (2014) Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction. J Adv Transp 48:250–271

    Google Scholar 

  54. Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neur Comput 31:1235–1270

    MathSciNet  Google Scholar 

  55. Zhang W, Yu Y, Qi Y, Shu F, Wang Y (2019) Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transportmetrica A: Trans Sci 15:1688–1711

    Google Scholar 

  56. Zhang J, Wang Y, Long M, Wang J, Wang H (2020) Predictive recurrent networks for seasonal spatiotemporal data with applications to urban computing. Chin J Comput 43:286–302

    CAS  Google Scholar 

  57. Zheng H, Lin F, Feng X, Chen Y (2021) A hybrid deep learning model with attention-based Conv-LSTM networks for short-term traffic flow prediction. IEEE Trans Intell Trans Syst 22:6910–6920

    Google Scholar 

  58. Zhu Z, Peng B, Xiong C, Zhang L (2016) Short-term traffic flow prediction with linear conditional Gaussian Bayesian network. J Adv Transp 50:1111–1123

    Google Scholar 

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Acknowledgements

This work described in this paper was supported in part by the National Natural Science Foundation of China (Grant nos. 62162012, 62173278, and 62072061), the Science and Technology Support Program of Guizhou Province, China (Grant no. QKHZC2021YB531), the Natural Science Research Project of Department of Education of Guizhou Province, China (Grant nos. QJJ2022015 and QJJ2022047), and the Scientific Research Platform Project of Guizhou Minzu University, China (Grant no. GZMUSYS[2021]04).

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Authors and Affiliations

  1. College of Data Science and Information Engineering, Guizhou Minzu University, 550025, Guiyang, China

    Dawen Xia, Yan Chen & Wenyong Zhang

  2. Department of Automotive Engineering, Guizhou Traffic Technician and Transportation College, 550008, Guiyang, China

    Yang Hu

  3. College of Computer Science, Chongqing University, 400044, Chongqing, China

    Yantao Li

  4. College of Electronic and Information Engineering, Southwest University, 400715, Chongqing, China

    Huaqing Li

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  1. Dawen Xia
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Xia, D., Chen, Y., Zhang, W. et al. RSAB-ConvGRU: A hybrid deep-learning method for traffic flow prediction. Multimed Tools Appl 83, 20559–20585 (2024). https://doi.org/10.1007/s11042-023-15877-x

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