Real-Time Estimation of Road Traffic Speeds from Cell-Based Vehicle Trajectories

  • Xiaoxiao Sun
  • Dongjin YuEmail author
  • Sai Liao
  • Wanqing Li
  • Chengbiao Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)


This paper presents a novel approach for urban road networks to estimate traffic speeds using vehicle trajectories captured by detectors on transportation cells. By scanning and analyzing dynamic traffic streams of passing-vehicles, we calculate the real-time traffic speed of road segment separated by adjacent detectors, which are further synthesized to present the traffic speed of whole road. Compared to driving routes data with limited coverage or floating GPS data with occasional missing that are both frequently utilized for traditional road speed estimation, our approach utilizes the full coverage detector data and is proved to have more accurate and reliable results in its application for two large cities of China. An analysis and visualization system was hence developed, whose successful operation in several transportation departments indicated the efficiency of our approach. It helps to guide travelers the optimal driving routes, which greatly relieves the huge traffic stress of city road.


Traffic trajectory Transportation cells Traffic speed Speed estimation 



The work is supported by National Natural Science Foundation of China (No. 61472112, No. 61702144), Key Science and Technology Project of Zhejiang Province (No. 2017C01010), and Natural Science Foundation of Zhejiang Province (No. LY12F02003).


  1. 1.
    Richards, P.I.: Shock waves on the highway. Oper. Res. 4(1), 42–51 (1956)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Han, J., Li, Z., Tang, L.A.: Mining moving object, trajectory and traffic data. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5982, pp. 485–486. Springer, Heidelberg (2010). Scholar
  3. 3.
    Wang, Z., Lu, M., Yuan, X., Zhang, J.: Visual traffic jam analysis based on trajectory data. IEEE Trans. Visual Comput. Graphics 19, 2159–2168 (2013)CrossRefGoogle Scholar
  4. 4.
    Lai, W.K., Kuo, T.H., Chen, C.H.: Vehicle speed estimation and forecasting methods based on cellular floating vehicle. Data. Appl. Sci. 6(2), 47 (2016)CrossRefGoogle Scholar
  5. 5.
    Liu, H., Sun, J.: Improving freeway traffic speed estimation using high-resolution loop detector data, Technical report. Center for Transportation Studies (2013)Google Scholar
  6. 6.
    Wang, Z., Ye, T., Lu, M., Yuan, X., Qu, H., Yuan, J., Wu, Q.: Visual exploration of sparse traffic trajectory data. IEEE Trans. Visual Comput. Graphics 20, 1813–1822 (2014)CrossRefGoogle Scholar
  7. 7.
    Bouillet, E., Ranganathan, A.: Scalable, real-time map-matching using IBM systems. In: Mobile Data Management, pp. 249–257 (2010)Google Scholar
  8. 8.
    Wang, H., Li, Z., Hurwitz, D., Shi, J.: Parametric modeling of the heteroscedastic traffic speed variance from loop detector data. J. Adv. Transp. 49(2), 279–296 (2015)CrossRefGoogle Scholar
  9. 9.
    Wang, J., Shi, Q.: Short-term traffic speed forecasting hybrid model based on chaos-wavelet analysis-support vector machine theory. Transp. Res. Part C 27(2), 219–232 (2013)CrossRefGoogle Scholar
  10. 10.
    Waller, S.T., Chiu, Y.C., Ruizjuri, N., et al.: Short Term Travel Time Prediction on Freeways in Conjunction with Detector Coverage Analysis. Austin (2007)Google Scholar
  11. 11.
    Willinger, W.: Traffic modeling for high-speed networks: theory versus practice. Inst. Math. Appl. 71, 395 (1995)zbMATHGoogle Scholar
  12. 12.
    Van, L.J., Hoogendoorn, S., Van, Z.H.: Robust and adaptive travel time prediction with neural networks. Technical report, Proceedings of the 6th Annual TRAIL Congress (Part 2) (2000)Google Scholar
  13. 13.
    Shan, Z., Zhao, D., Xia, Y.: Urban road traffic speed estimation for missing probe vehicle data based on multiple linear regression model. In: International IEEE Conference on Intelligent Transportation Systems, pp. 118–123. IEEE (2013)Google Scholar
  14. 14.
    Grundy, C., Steinbach, R., Edwards, P., et al.: Effect of 20 mph traffic speed zones on road injuries in London, 1986–2006: controlled interrupted time series analysis. Br. Med. J. 340(7736), 31 (2010)Google Scholar
  15. 15.
    Wang, Y., Papageorgiou, M., Messmer, A.: Real-time freeway traffic state estimation based on extended Kalman filter: adaptive capabilities and real data testing. Transp. Res. Part A 42(10), 1340–1358 (2008)Google Scholar
  16. 16.
    Shi, D.X., Ding, T.J., Ding, B., et al.: Traffic speed forecasting method based on nonparametric regression. Comput. Sci. 43(2), 224–229 (2016). In ChineseGoogle Scholar
  17. 17.
    Jiang, X., Adeli, H.: Dynamic wavelet neural network model for traffic flow forecasting. J. Transp. Eng. 131(10), 771–779 (2005)CrossRefGoogle Scholar
  18. 18.
    Rahmani, M., Koutsopoulos, H.N., Jenelius, E.: Travel time estimation from sparse floating car data with consistent path inference: a fixed point approach. Transp. Res. Part C Emerg. Technol. 85, 628–643 (2015)CrossRefGoogle Scholar
  19. 19.
    Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 336–343. ACM (2009)Google Scholar
  20. 20.
    Kim, K., Seol, S., Kong, S.H.: High-speed train navigation system based on multi-sensor data fusion and map matching algorithm. Int. J. Control Autom. Syst. 13(3), 503–512 (2015)CrossRefGoogle Scholar
  21. 21.
    Marchal, F., Hackney, J., Axhausen, K.W.: Efficient map matching of large global positioning system data sets: tests on speed-monitoring experiment in Zürich. Transp. Res. Rec. J. Transp. Res. Board 1935(1), 93–100 (2005)CrossRefGoogle Scholar
  22. 22.
    Yue, Y., Zou, H.X., Li, Q.Q.: Urban road travel speed estimation based on low sampling floating car data. In: International Conference of Chinese Transportation Professionals, pp. 1–7 (2009)Google Scholar
  23. 23.
    Shan, Z., Zhao, D., Xia, Y.: Urban road traffic speed estimation for missing probe vehicle data based on multiple linear regression models. In: International IEEE Conference on Intelligent Transportation Systems, pp. 118–123. IEEE (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xiaoxiao Sun
    • 1
  • Dongjin Yu
    • 1
    Email author
  • Sai Liao
    • 1
  • Wanqing Li
    • 1
  • Chengbiao Zhou
    • 2
  1. 1.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina
  2. 2.Hangzhou Trustway Technology Company LimitedHangzhouChina

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