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Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN

  • Research Article - Computer Engineering and Computer Science
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Abstract

A short-term traffic flow prediction framework is proposed for urban road networks based on data-driven methods that mainly include two modules. The first module contains a set of algorithms to process traffic flow data. After analysis and repair, a complete data set without outliers is provided as well as a data set containing pairs of road segments that are the most similar to each other in regard to their trends. The second module focuses on multiple time-step short-term forecasting. With a good understanding of the periodicity and randomness of traffic flow, the time series is first decomposed into a trend series and residual series. After reconstructing the two time series, model training and prediction based on a long short-term memory-recurrent neural network (LSTM-RNN) are performed. Finally, the two results are combined together to form the final prediction. A model evaluation is performed using two urban road networks. The results show that the data processing module can effectively improve the data quality, reduce the training time and enhance the model robustness. The LSTM-RNN correctly identifies the time trend and spatial similarity of traffic flow and obtains a more accurate multiple time-step prediction. The proposed framework outperforms other deep learning algorithms and has better accuracy and stability.

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References

  1. Zhang, J.; et al.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 4(12), 1624–1639 (2011)

    Article  Google Scholar 

  2. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 1(2), 1–127 (2009)

    Article  MATH  Google Scholar 

  3. Liu, W.; Ma, T.; Xie, Q.; et al.: LMAE: a large margin auto-encoders for classification. Signal Process. 141, 137–143 (2017)

    Article  Google Scholar 

  4. Prusa, J.D.; Khoshgoftaar, T.M.: Improving deep neural network design with new text data representations. J. Big Data 4(1), 7 (2017)

    Article  Google Scholar 

  5. Wang, W.; Jiang, Y.; Wang, D.; et al.: Through wall human detection under small samples based on deep learning algorithm. Pattern Recognit. 72, 458–465 (2017)

    Article  Google Scholar 

  6. Jalali, A.; Mallipeddi, R.; Lee, M.: Sensitive deep convolutional neural network for face recognition at large standoffs with small dataset. Expert Syst. Appl. 87, 304–315 (2017)

    Article  Google Scholar 

  7. Vlahogianni, E.I.; Karlaftis, M.G.; Golias, J.C.: Short-term traffic forecasting: where we are and where we’re going. Transp. Res. Part C Emerg. Technol. 43, 3–19 (2014)

    Article  Google Scholar 

  8. Chan, K.; Dillon, T.; Singh, J.; et al.: Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm. IEEE Trans. Intell. Transp. Syst. 13, 644–654 (2015)

    Article  Google Scholar 

  9. Fusco, G.; Colombaroni, C.; Comelli, L.; et al.: Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models. In: Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015 International Conference on IEEE, pp. 93-101. (2015)

  10. Lint, J.W.C.; Hooqendoorn, S.P.; Zuvlen, H.J.V.: Freeway travel time prediction with state-space neural networks: modeling state-space dynamics with recurrent neural networks. Transp. Res. Rec. J. Transp. Res. Board 1811(1), 347–369 (2002)

    Google Scholar 

  11. Ishak, S.; Kotha, P.; Alecsandru, C.: Optimization of dynamic neural network performance for short-term traffic prediction. Transp. Res. Rec. J Transp. Res. Board 1836, 45–56 (2003)

    Article  Google Scholar 

  12. Liu, H.; Zuylen, H.V.; Lint, H.V.; et al.: Predicting urban arterial travel time with state-space neural networks and Kalman filters. Transp. Res. Rec. J. Transp. Res. Board 1968, 99–108 (2006)

    Article  Google Scholar 

  13. Yin, H.; Wong, S.C.; Xu, J.; et al.: Urban traffic flow prediction using a fuzzy-neural approach. Transp. Res. Part C Emerg. Technol. 10(2), 85–98 (2002)

    Article  Google Scholar 

  14. Park, B.: Hybrid neuro-fuzzy application in short-term freeway traffic volume forecasting. Transp. Res. Rec. J. Transp. Res. Board 1802(8), 190–196 (2002)

    Article  Google Scholar 

  15. Quek, C.; Pasquier, M.; Lim, B.B.S.: POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction. IEEE Transp. Intell. Transp. Syst. 7(2), 133–146 (2006)

    Article  Google Scholar 

  16. Tang, J.J.; Liu, F.; Zou, Y.J.; et al.: An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. Trans. Intell. Transp. Syst. 9(18), 2340–2350 (2017)

    Article  Google Scholar 

  17. Meng, M.; Shao, C.; Wong, Y.; et al.: A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques. J. Cent. South Univ. 22, 779–786 (2015)

    Article  Google Scholar 

  18. Tang, J.; Liu, F.; Zhang, W.; et al.: Exploring dynamic property of traffic flow time series in multi-states based on complex networks: Phase space reconstruction versus visibility graph. Phys. A Stat. Mech. Appl. 450, 635–648 (2016)

    Article  Google Scholar 

  19. Mei, H.; Ma, A.; Poslad, S.; et al.: Short-term traffic volume prediction for sustainable transportation in an urban area. J. Comput. Civ. Eng. 29(2), 04014036 (2013)

    Article  Google Scholar 

  20. Van, W.C.; Van, H.C.: Short-term traffic and travel time prediction models. Artif. Intell. Appl. Crit. Transp. Issues 22, 22–41 (2012)

    Google Scholar 

  21. Nie, L.; Jiang, D.; Guo, L.; et al.: Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks. J. Netw. Comput. Appl. 76(C), 16–22 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Fouladgar, M.; Parchami, M.; Elmasri, R. et al.: Scalable deep traffic flow neural networks for urban traffic congestion prediction, pp. 2251–2258 (2017). https://doi.org/10.1109/IJCNN.2017.7966128

  24. Wei, D.: Network traffic prediction based on RBF neural network optimized by improved gravitation search algorithm. Neural Comput. Appl. 28, 1–10 (2016)

    Article  Google Scholar 

  25. Ma, X.; Yu, H.; Wang, Y.; et al.: Large-scale transportation network congestion evolution prediction using deep learning theory. Plos One 10(3), e0119044 (2015)

    Article  Google Scholar 

  26. Ma, X.; Dai, Z.; He, Z.; et al.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818 (2017)

    Article  Google Scholar 

  27. Yu, H.; Wu, Z.; Wang, S.; et al.: Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7), 1501 (2017)

    Article  Google Scholar 

  28. Zhao, Z.; Chen, W.; Wu, X.; et al.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68–75 (2017)

    Article  Google Scholar 

  29. Wang, J.; Wang, Y.; Yun, M.; et al.: Development of urban road network traffic state dynamic estimation method. Math. Probl. Eng. 2015(11), 1–10 (2015)

    Google Scholar 

  30. Yang, Q.; Wang, J.; Song, X.; et al.: Urban traffic congestion prediction using floating car trajectory data In: International Conference on Algorithms and Architectures for Parallel Processing, pp. 18–30. Springer International Publishing, Berlin (2015)

  31. Kanarachos, S.; Christopoulos, S.R.G.; Chroneos, A.; et al.: Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform. Expert Syst. Appl. 85, 292–304 (2017)

    Article  Google Scholar 

  32. Qi, H.; Liu, M.; Wang, D.; et al.: Spatial-temporal congestion identification based on time series similarity considering missing data. Plos One 11(9), e0162043 (2016)

    Article  Google Scholar 

  33. Li, L.; Li, Y.; Li, Z.: Efficient missing data imputing for traffic flow by considering temporal and spatial dependence. Transp. Res. Part C 34(9), 108–120 (2013)

    Article  Google Scholar 

  34. Jiang, S., Wang, S., Li, Z., et al.: Fluctuation similarity modeling for traffic flow time series: a clustering approach. In: IEEE, International Conference on Intelligent Transportation Systems, pp. 848–853. IEEE (2015)

  35. Yang, Z.; Bing, Q.; Lin, C.; et al.: Research on short-term traffic flow prediction method based on similarity search of time series. Math. Probl. Eng. 2014(7), 1–8 (2014)

    Google Scholar 

  36. Wang, X.; Ling, P.; Chi, T.: A method of urban traffic flow speed estimation using sparse floating car data. Acta Geod. Cartogr. Sin. 45(7), 866–873 (2016)

    Google Scholar 

  37. Jang, S.C.; Guan, W.: Division of urban traffic road section based on clustering analysis. J. Transp. Syst. Eng. Inf. Technol. 9(3), 36–42 (2009)

    Google Scholar 

  38. Xin, F.F.; Chen, X.H.; Lin, H.F.: Research on time space distribution characteristics of floating car data in road network. China J. Highw. Transp. 21(4), 105–110 (2008)

    Google Scholar 

  39. Salvador, S.; Chan, P.: Toward accurate dynamic time warping in linear time and space. In: KDD Workshop on Mining Temporal and Sequential Data, pp. 70–80. (2004)

  40. John, P.: Time Series Analysis with Applications in R, vol. 38, 2nd edn, pp. 1311–1312. Springer, New York (2011)

    Google Scholar 

  41. Li, L.; Su, X.; Zhang, Y.; et al.: Traffic prediction, data compression, abnormal data detection and missing data imputation: An integrated study based on the decomposition of traffic time series. In: IEEE, International Conference on Intelligent Transportation Systems, pp. 282–289. IEEE (2014)

  42. Zhang, L.; Huang, S.G.; Shi, Z.X.; et al.: CAPTCHA recognition method based on RNN of LSTM. Pattern Recognit. Artif. Intell. 24(1), 40–47 (2011)

    Google Scholar 

  43. Lai, J.; Chen, B.; Tan, T.; et al.: Phone-aware LSTM-RNN for voice conversion. In: Signal Processing (ICSP), 2016 IEEE 13th International Conference on, pp. 177–182. IEEE (2016)

  44. Li, D.; Qian, J.: Text sentiment analysis based on long short-term memory. In: IEEE International Conference on Computer Communication and the Internet, pp. 471–475. IEEE (2016)

  45. Graves, A.: Supervised sequence labelling with recurrent neural networks. In: Studies in Computational Intelligence. Springer, Berlin (2012)

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Acknowledgements

The work presented in this paper was jointly supported by the National Natural Science Foundation of China (61263024) and the National Natural Science Foundation of Guangdong Province (2016A030310104). The authors would like to thank Zhou Yong from the Shenzhen Urban Transport Planning Centre for providing the experimental data samples.

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Authors and Affiliations

  1. School of Civil Engineering and Transportation, Traffic Information Engineering & Control, South China University of Technology, Wushan Street, no. 381, Tianhe District, Guangzhou, Guangdong, China

    Wang Xiangxue, Xu Lunhui & Chen Kaixun

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  1. Wang Xiangxue
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  2. Xu Lunhui
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  3. Chen Kaixun
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Correspondence to Wang Xiangxue.

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Xiangxue, W., Lunhui, X. & Kaixun, C. Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN. Arab J Sci Eng 44, 3043–3060 (2019). https://doi.org/10.1007/s13369-018-3390-0

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  • DOI: https://doi.org/10.1007/s13369-018-3390-0

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