Advertisement

Urban Traffic Congestion Prediction Using Floating Car Trajectory Data

  • Qiuyuan Yang
  • Jinzhong Wang
  • Ximeng Song
  • Xiangjie KongEmail author
  • Zhenzhen Xu
  • Benshi Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9529)

Abstract

Traffic congestion prediction is an important precondition to promote urban sustainable development. Nevertheless, there is a lack of a unified prediction method to address the performance metrics, such as accuracy, instantaneity and stability, systematically. In the paper, we propose a novel approach to predict the urban traffic congestion efficiently with floating car trajectory data. Specially, an innovative traffic flow prediction method utilizing particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens’ cognitive congestion states. We conduct extensive experiments with real floating car data and the experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability.

Keywords

Floating car Particle swarm optimization Traffic congestion prediction Traffic flow prediction Fuzzy comprehensive evaluation 

Notes

Acknowledgments

This work was partially supported by the Natural Science Foundation of China under Grants No. 61203165 and No. 61174174, the Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P.R. China No. SCIP2012001, and the Fundamental Research Funds for Central Universities.

References

  1. 1.
    Zheng, Y., Capra, L., Wolfson, O., et al.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. (TIST) 5(3), 38 (2014)Google Scholar
  2. 2.
    Younes, M.B., Boukerche, A.: A performance evaluation of an efficient traffic congestion detection protocol (ECODE) for intelligent transportation systems. Ad Hoc Netw. 24, 317–336 (2015)CrossRefGoogle Scholar
  3. 3.
    Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (2015). doi: 10.1145/2743025 Google Scholar
  4. 4.
    Kong, Q.J., Zhao, Q., Wei, C., et al.: Efficient traffic state estimation for large-scale urban road networks. IEEE Trans. Intell. Transp. Syst. 14(1), 398–407 (2013)CrossRefGoogle Scholar
  5. 5.
    Kong, Q.J., Li, Z., Chen, Y., et al.: An approach to urban traffic state estimation by fusing multisource information. IEEE Trans. Intell. Transp. Syst. 10(3), 499–511 (2009)CrossRefGoogle Scholar
  6. 6.
    Zhang, J.D., Xu, J., Liao, S.S.: Aggregating and sampling methods for processing GPS data streams for traffic state estimation. IEEE Trans. Intell. Transp. Syst. 14(4), 1629–1641 (2013)CrossRefGoogle Scholar
  7. 7.
    Li, L., Chen, X., Zhang, L.: Multimodel ensemble for freeway traffic state estimations. IEEE Trans. Intell. Transp. Syst. 15(3), 1323–1336 (2014)CrossRefGoogle Scholar
  8. 8.
    Feng, Y., Hourdos, J., Davis, G.A.: Probe vehicle based real-time traffic monitoring on urban roadways. Transp. Res. Part C Emerg. Technol. 40, 160–178 (2014)CrossRefGoogle Scholar
  9. 9.
    Shankar, H., Raju, P.L.N., Rao, K.R.M.: Multi model criteria for the estimation of road traffic congestion from traffic flow information based on fuzzy logic. J. Transp. Technol. 2, 50 (2012)CrossRefGoogle Scholar
  10. 10.
    Xu, Y., Wang, B., Kong, Q., et al.: Spatio-temporal variable selection based support vector regression for urban traffic flow prediction. In: Proceeding of the 93rd Annual Meeting of the Transportation Research Board, Washington, DC, pp. 14–1994 (2014)Google Scholar
  11. 11.
    Hong, W.C., Dong, Y., Zheng, F., et al.: Hybrid evolutionary algorithms in a SVR traffic flow forecasting model. Appl. Math. Comput. 217(15), 6733–6747 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Li, M.W., Hong, W.C., Kang, H.G.: Urban traffic flow forecasting using Gauss–SVR with cat mapping, cloud model and PSO hybrid algorithm. Neuro Comput. 99, 230–240 (2013)Google Scholar
  13. 13.
    Wang, J., Shi, Q.: Short-term traffic speed forecasting hybrid model based on chaos-wavelet analysis-support vector machine theory. Transp. Res. Part C Emerg. Technol. 27, 219–232 (2013)CrossRefGoogle Scholar
  14. 14.
    Wang, F., Tan, G., Deng, C., et al.: Real-time traffic flow forecasting model and parameter selection based on ε-SVR. In: Proceedings of the 7th IEEE World Congress on Intelligent Control and Automation, pp. 2870–2875. Chongqing, China (2008)Google Scholar
  15. 15.
    Chen, H., Rakha, H.A., Sadek, S.: Real-time freeway traffic state prediction: a particle filter approach. In: Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 626–631. Washington, DC, USA (2011)Google Scholar
  16. 16.
    Dunne, S., Ghosh, B.: Regime-based short-term multivariate traffic condition forecasting algorithm. J. Transp. Eng. 138(4), 455–466 (2011)CrossRefGoogle Scholar
  17. 17.
    Min, W., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transp. Res. Part C Emerg. Technol. 19(4), 606–616 (2011)CrossRefGoogle Scholar
  18. 18.
    Zhang, X., Onieva, E., Perallos, A., et al.: Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction. Transp. Res. Part C Emerg. Technol. 43(1), 127–142 (2014)CrossRefGoogle Scholar
  19. 19.
    Herring, R., Hofleitner, A., Amin, S., et al.: Using mobile phones to forecast arterial traffic through statistical learning. In: Proceedings of the 89th Transportation Research Board Annual Meeting, pp. 10–2493. Washington DC, USA (2010)Google Scholar
  20. 20.
    Castro, P.S., Zhang, D., Li, S.: Urban traffic modelling and prediction using large scale taxi GPS traces. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds.) Pervasive 2012. LNCS, vol. 7319, pp. 57–72. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Zhou, X., Wang, W., Yu, L.: Traffic flow analysis and prediction based on GPS data of floating cars. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds.) Information Technology. LNEE, vol. 210, pp. 497–508. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  22. 22.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)CrossRefGoogle Scholar
  23. 23.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4(2), 1942–1948 (1995)CrossRefGoogle Scholar
  24. 24.
  25. 25.
    Beijing Traffic Development Research Center. The transportation development annual report at 2012 of Beijing city. http://www.bjtrc.org.cn/JGJS.aspx?id=5.2&Menu=GZCG (2012)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Qiuyuan Yang
    • 1
  • Jinzhong Wang
    • 1
  • Ximeng Song
    • 1
  • Xiangjie Kong
    • 1
    • 2
    • 3
    Email author
  • Zhenzhen Xu
    • 1
  • Benshi Zhang
    • 1
  1. 1.School of SoftwareDalian University of TechnologyDalianChina
  2. 2.Key Laboratory of System Control and Information ProcessingMinistry of EducationShanghaiChina
  3. 3.Key Laboratory of Control Engineering of Henan ProvinceHenan Polytechnic UniversityJiaozuoChina

Personalised recommendations