Speed-Volume Relationship Model for Speed Estimation on Urban Roads in Intelligent Transportation Systems

  • Zilu LiangEmail author
  • Yasushi Wakahara
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 366)


Estimating average speed on roads is required by many applications in Intelligent Transportation Systems. In spite of abundant researches done on speed estimation on highways, there is only limited effort made in urban traffic networks. Current reserach methodology is to apply highway models to urban roads directly or under trivial modification. In this paper, we propose a novel speed estimation model tailored for urban roads. The contribution of this work includes the following two aspects: 1)we demonstrate that application of modified highway models to urban roads is not always an effective methodology; 2)we propose a speed-volume relationship model tailored for speed estimation on urban roads by incorporating the impedance effect of exit intersection of a concerned road. We have applied the model to estimate the speed in Cologne, Germany, compared the accuracy between the proposed model and a slightly modified Greenshield’s Model, and confirmed its effectivity as well as superiority.


Traffic modeling speed estimation urban traffic network intelligent transportation system 


  1. 1.
    Jayakrishnan, R., Tsai, W.K., Chen, A.: A dynamic traffic assignment model with traffic-flow relationships. Transportation Research-C 3 (1995) 51-72.CrossRefGoogle Scholar
  2. 2.
    Balakrishna, R.: Calibration of the demand simulator in dynamic traffic assignment system. Master Thesis, MIT (2002).Google Scholar
  3. 3.
    Liang, Z., Wakahara, Y.: Real-time urban traffic amount prediction models for dynamic route guidance systems. EURASIP Journal on Wireless Communications and Networking (2014).Google Scholar
  4. 4.
    Daganzo, D.F.: Queue spillovers in transportation networks with a route choice. Transportation Science 32 (1998) 3-11.CrossRefzbMATHGoogle Scholar
  5. 5.
    Greenshields, B.D.: A study in highway capacity. Highway Research Board, Proceedings 14 (1935) 458.Google Scholar
  6. 6.
    Dhamaniya, A., Chandra, S.: Speed prediction models for urban arterials under mixed traffic conditions. Procedia-Social and Behavioral Sciences 104 (2013) 342-351.CrossRefGoogle Scholar
  7. 7.
    Pan, T.L.; Sumalee, A.; Zhong, R.X.; Indra-Payoong, N., ”Short-Term Traffic State Prediction Based on TemporalSpatial Correlation,” IEEE Transactions on Intelligent Transportation Systems 14 (2013) 1242-1254.Google Scholar
  8. 8.
    Sivaraman, S.; Trivedi, M.M.,: Integrated Lane and Vehicle Detection, Localization and tracking: a synergistic approach. IEEE Transactions on Intelligent Transportation Systems 14 (2013) 906-917.Google Scholar
  9. 9.
    Choi, J.M.: Multi-touch based standard UI design of car navigation system for providing information of surrounding areas. Design, User Experience, and Usability, User Experience in Novel Technological Environments. Lecture Notes in Computer Science 8014 (2013) 40-48.Google Scholar
  10. 10.
    Krajzewicz, D., Erdmann, J., Behrisch, M., and Bieker, L.: Recent development and applications of SUMO Simulation of Urban MObility. International Journal on Advances in Systems and Measurements 5 (2012) 128-138.Google Scholar
  11. 11.
  12. 12.
    Gazis, D.C.: Optimum control of a system of oversaturated intersections. Oper. Res. 12 (1964) 815-831.CrossRefzbMATHGoogle Scholar
  13. 13.
    Hazelton, M.L.: Estimating vehicle speed from traffic count and occupancy data. Journal of Data Science 2 (2004) 231-244.Google Scholar
  14. 14.
    Flores, B.E.: A pragmatic view of accuracy measurement in forecasting. Omega Int. J. of Mgmt Sci. 14 (1986) 93-98.CrossRefGoogle Scholar
  15. 15.
    Hyndman, R.J.: Another look at forecast accuracy metrics for intermittent demand. International Journal of Applied Forecasting 2006 (2006) 43-46.MathSciNetGoogle Scholar
  16. 16.
    Pan, J., Popa, J.S., Zeitouni, K., and Borcea, C.: Proactive vehicular traffic rerouting for lower travel time. IEEE Trans. Veh. Tech. 62 (2013) 3551-3568.CrossRefGoogle Scholar
  17. 17.
    Greenberg, H.: An analysis of traffic flow. Operations Research 7 (1959) 79-85.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Underwood, R.T.: Speed, volume, and density relationships: quality and theory of traffic flow. Yale Bureau of Highway Traffic (1961) 141-188.Google Scholar
  19. 19.
    Rakha, H., Growther, B.: Comparison of Greenshields, Pipes, and Van Aerde car-following and traffic stream models. Transportation Research Record 1802/2002 (2007) 248-262.Google Scholar
  20. 20.
    Hensher, D.A., Button, K.J.: Handbook of trnasport modelling [Chapter 10]. Elsevier, UK, 2008.Google Scholar
  21. 21.
    Castillo, J.M.: Three new models for the flow-density relationship: derivation and testing for freeway and urban data. Transportmetrica 8 (2012) 443-465.CrossRefGoogle Scholar
  22. 22.
    Liang, Z., Wakahara, Y.: City traffic prediction based on real-time traffic information for intelligent transportation systems. Proc. of ITST (2013) 378-383.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School of EngineeringThe University of TokyoBunkyo-KuJapan

Personalised recommendations