Towards Adaptive Sensory Data Fusion for Detecting Highway Traffic Conditions in Real Time

  • Yanling Cui
  • Beihong JinEmail author
  • Fusang Zhang
  • Tingjian Ge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)


The key challenge of detecting highway traffic conditions is to achieve it in a fully-covered, high-accuracy, low-cost and real-time manner. We present an approach named Megrez on the basis of treating mobile phones and probe vehicles as roving sensors, loop detectors as static sensors. Megrez can admit one or multiple types of data, including signaling data in a mobile communication network, data from loop detectors, and GPS data from probe vehicles, to carry out the traffic estimation and monitoring. In order to accurately reconstruct traffic conditions with full road segment coverage, Megrez provides a practical way to overcome the sparsity and incoherence of sensory data and recover the missing data in light of recent progresses in compressive sensing. Moreover, Megrez incorporates the characteristics of traffic flows to rectify the estimates. Using large-scale real-world data as input, we conduct extensive experiments to evaluate Megrez. The experimental results show that, in contrast to three other fusion methods, the results from our approach have high precisions and recalls. In addition, Megrez keeps the errors of estimates low even when not all three types of data are available.


Data fusion Traffic condition detection Mobile signaling Compressive sensing Adaptation 



This work was supported by the National Natural Science Foundation of China under Grant No. 61472408 and the Ministry of Transportation of China under Grant No. 2015315Q16080. Tingjian Ge was supported in part by the NSF grants IIS-1149417 and IIS-1633271.


  1. 1.
    Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)CrossRefGoogle Scholar
  2. 2.
    Russell, T.: Signaling System 7, 6th edn. McGraw-Hill Education, New York (2014)Google Scholar
  3. 3.
    Deng, D., Shahabi, C., Demiryurek, U., Zhu, L., Yu, R., Liu, Y.: Latent space model for road networks to predict time-varying traffic. In: ACM International Conference on Knowledge Discovery and Data Mining, pp. 1525–1534 (2016)Google Scholar
  4. 4.
    Hajimolahoseini, H., Amirfattahi, R., Soltanian-Zadeh, H.: Robust vehicle tracking algorithm for nighttime videos captured by fixed cameras in highly reflective environments. Comput. Vis. IET 8(6), 535–544 (2014)CrossRefGoogle Scholar
  5. 5.
    Wang, F., Hu, L., Zhou, D., Sun, R., Hu, J., Zhao, K.: Estimating online vacancies in real-time road traffic monitoring with traffic sensor data stream. Ad Hoc Netw. 35(C), 3–13 (2015)CrossRefGoogle Scholar
  6. 6.
    Zhu, H., Zhu, Y., Li, M., Ni, L.M.: SEER: metropolitan-scale traffic perception based on lossy sensory data. In: IEEE International Conference on Computer Communications, Rio De Janeiro, Brazil, pp. 217–225 (2009)Google Scholar
  7. 7.
    Zhu, Y., Li, Z., Zhu, H., Li, M., Zhang, Q.: A compressive sensing approach to urban traffic estimation with probe vehicles. IEEE Trans. Mob. Comput. 12(11), 2289–2302 (2013)CrossRefGoogle Scholar
  8. 8.
    Liu, Z., Li, Z., Li, M., Xing, W.: Mining road network correlation for traffic estimation via compressive sensing. IEEE Trans. Intell. Transp. Syst. 17(7), 1–12 (2016)CrossRefGoogle Scholar
  9. 9.
    Zhan, X., Zheng, Y., Yi, X., Ukkusuri, S.V.: Citywide traffic volume estimation using trajectory data. IEEE Trans. Knowl. Data Eng. 29(2), 272–285 (2017)CrossRefGoogle Scholar
  10. 10.
    Huang-Fu, C.C., Lin, Y.B.: Deriving vehicle speeds from standard statistics of mobile telecom switches. IEEE Trans. Vehic. Technol. 61(7), 3337–3341 (2012)CrossRefGoogle Scholar
  11. 11.
    Janecek, A., Valerio, D., Hummel, K.A., Ricciato, F., Hlavacs, H.: The cellular network as a sensor: from mobile phone data to real-time road traffic monitoring. IEEE Trans. Intell. Transp. Syst. 16(5), 2551–2572 (2015)CrossRefGoogle Scholar
  12. 12.
    Becker, R.A., Caceres, R., Hanson, K., Ji, M.L., Urbanek, S., Varshavsky, A., Volinsky, C.: Route classification using cellular handoff patterns. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 123–132 (2011)Google Scholar
  13. 13.
    Caceres, N., Romero, L.M., Benitez, F.G., Del Castillo, J.M.: Traffic flow estimation models using cellular phone data. IEEE Trans. Intell. Transp. Syst. 13(3), 1430–1441 (2012)CrossRefGoogle Scholar
  14. 14.
    Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., Ratti, C.: Real-Time urban monitoring using cell phones: a case study in Rome. IEEE Trans. Intell. Transp. Syst. 12(1), 141–151 (2011)CrossRefGoogle Scholar
  15. 15.
    Aslam, J., Lim, S., Pan, X., Rus, D.: City-scale traffic estimation from a roving sensor network. ACM Conference on Embedded Network Sensor Systems, pp. 141–154 (2012)Google Scholar
  16. 16.
    Bouillet, E., Chen, B., Cooper, C., Dahlem, D., Verscheure, O.: Fusing traffic sensor data for real-time road conditions. In: International Workshop on Sensing and Big Data Mining, pp. 1–6 (2013)Google Scholar
  17. 17.
    Yang, L., Pereira, F.C., Seshadri, R., O’Sullivan, A., Antoniou, C., Ben-Akiva, M.: DynaMIT2.0: architecture design and preliminary results on real-time data fusion for traffic prediction and crisis management. In: IEEE 18th International Conference on Intelligent Transportation Systems, pp. 2250–2255 (2015)Google Scholar
  18. 18.
    Candés, E.J., Tao, T.: The power of convex relaxation: near-optimal matrix completion. IEEE Trans. Inf. Theory 56(5), 2053–2080 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Bach, F.R.: Consistency of trace norm minimization. J. Mach. Learn. Res. 9(2) (2008)Google Scholar
  20. 20.
    Fazel, M.: Matrix rank minimization with applications. Ph.D. dessertation, Department of electrical engineering, Stanford University, California (2002)Google Scholar
  21. 21.
    Recht, B., Fazel, M., Parrilo, P.A.: Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. Siam Rev. 52(3), 471–501 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
  23. 23.
    Cassidy, M.J., Windover, J.R.: Methodology for assessing dynamics of freeway traffic flow. Transp. Res. Rec. 1484, 73–79 (1995)Google Scholar
  24. 24.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82, 35–45 (1960)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yanling Cui
    • 1
    • 2
  • Beihong Jin
    • 1
    • 2
    Email author
  • Fusang Zhang
    • 1
    • 2
  • Tingjian Ge
    • 3
  1. 1.State Key Laboratory of Computer Sciences, Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.University of MassachusettsLowellUSA

Personalised recommendations