Skip to main content
SpringerLink
Log in

Estimation of origin–destination matrices using link counts and partial path data

  • Published:
Transportation Aims and scope Submit manuscript

Abstract

After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a multiobjective model which utilizes combinations of vehicle counts and partial path data. The methods were tested to estimate the origin–destination matrix of a large-scale network from Mashhad City with 163 traffic zones and 2093 links, and the results were compared with the conventional gradient-based algorithm. The results show that the innovative methods performed better as compared to the gradient-based algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Asakura, Y., Hato, E., Kashiwadani, M.: Origin–destination matrices estimation model using automatic vehicle identification data and its application to the Han-Shin expressway network. Kluwer Acad. Publ. 27, 419–438 (2000)

    Google Scholar 

  • Ashok, K.: Estimation and prediction of time-dependent origin–destination flows. Ph.D. Thesis, Massachusetts Inst. Technol. USA. (1996). https://doi.org/10.1287/trsc.36.2.184.563

  • Baek, S., Lim, Y., Rhee, S., Choi, K.: Method for estimating population OD matrix based on probe vehicles. KSCE J. Civ. Eng. 14, 231–235 (2010). https://doi.org/10.1007/s12205-010-0231-4

    Article  Google Scholar 

  • Bera, S., Rao, K.V.K.: Estimation of origin-destination matrix from traffic counts: the state of the art. Eur. Transp. Trasp. Eur. 49, 3–23 (2011)

    Google Scholar 

  • Cantelmo, G., Cipriani, E., Gemma, A., Nigro, M.: An adaptive bi-level gradient procedure for the estimation of dynamic traffic demand. IEEE Trans. Intell. Transp. Syst. 15, 1348–1361 (2014). https://doi.org/10.1109/TITS.2014.2299734

    Article  Google Scholar 

  • Carrese, S., Cipriani, E., Mannini, L., Nigro, M.: Dynamic demand estimation and prediction for traffic urban networks adopting new data sources. Transp. Res. Part C Emerg. Technol. 81, 83–98 (2017). https://doi.org/10.1016/j.trc.2017.05.013

    Article  Google Scholar 

  • Castillo, E., Menéndez, J.M., Jiménez, P.: Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations. Transp. Res. Part B Methodol. 42, 455–481 (2008). https://doi.org/10.1016/j.trb.2007.09.004

    Article  Google Scholar 

  • Dixon, M.P., Rilett, L.R.: Population origin–destination estimation using automatic vehicle identification and volume data. J. Transp. Eng. 131, 75–82 (2005). https://doi.org/10.1061/(ASCE)0733-947X(2005)131:2(75)

    Article  Google Scholar 

  • Doblas, J., Benitez, F.G.: An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix. Transp. Res. Part B Methodol. 39, 565–591 (2005). https://doi.org/10.1016/j.trb.2004.06.006

    Article  Google Scholar 

  • Fu, C., Zhu, N., Ling, S., Ma, S., Huang, Y.: Heterogeneous sensor location model for path reconstruction. Transp. Res. Part B 91, 77–97 (2016). https://doi.org/10.1016/j.trb.2016.04.013

    Article  Google Scholar 

  • Gentile, G.: Local user cost equilibrium: a bush-based algorithm for traffic assignment. Transp. A Transp. Sci. 10, 15–54 (2014). https://doi.org/10.1080/18128602.2012.691911

    Article  Google Scholar 

  • Gentile, G., Noekel, K.: Linear user cost equilibrium: a new algorithm for traffic assignment. In: European Transport Conference, At Leeuwenhorst Conference Centre, The Netherlands, pp. 1–52 (2009)

  • Hadavi, M., Shafahi, Y.: Vehicle identification sensor models for origin–destination estimation. Transp. Res. Part B Methodol. 89, 82–106 (2016). https://doi.org/10.1016/j.trb.2016.03.011

    Article  Google Scholar 

  • ITSR: Mashhad Comprehensive Transportation Studies. Sharif University of Technology, Tehran (1995)

    Google Scholar 

  • Jamali, A.: O–D demand estimation base on automatic vehicle identification data. Master Thesis, Sharif University Technology, Iran (2014)

  • Kwon, J., Varaiya, P.: Real-time estimation of origin–destination matrices with partial trajectories from electronic toll collection tag data. Transp. Res. Rec. J. Transp. Res. Board. 1923, 119–126 (2005). https://doi.org/10.3141/1923-13

    Article  Google Scholar 

  • LeBlanc, L.J., Farhangian, K.: Selection of a trip table which reproduces observed link flows. Transp. Res. Part B 16, 83–88 (1982)

    Article  Google Scholar 

  • Nguyen, S.: Estimating an OD matrix from network data: a network equilibrium approach. Universite de Montreal, Centre de Recherche sur les Transports, Montreal (1977)

    Google Scholar 

  • Parry, K., Hazelton, M.L.: Estimation of origin–destination matrices from link counts and sporadic routing data. Transp. Res. Part B Methodol. 46, 175–188 (2012). https://doi.org/10.1016/j.trb.2011.09.009

    Article  Google Scholar 

  • Sheffi, Y.: Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice-Hall, Inc., Englewood Cliffs (1985)

    Google Scholar 

  • Spiess, H.: A gradient approach for the O–D matrix adjustment problem. Centre for Research on Transportation, University of Montreal, Montreal (1990)

    Google Scholar 

  • Talebian Yazdi, P.: Solving location problem for vehicle identification sensors to observe or estimate path flows in large-scale networks. Master Thesis, Sharif University Technology, Iran (2018)

  • Vasko, F.J., Lu, Y., Zyma, K.: What is the best greedy-like heuristic for the weighted set covering problem? Oper. Res. Lett. 44, 366–369 (2016). https://doi.org/10.1016/j.orl.2016.03.007

    Article  Google Scholar 

  • Yang, X., Lu, Y., Hao, W.: Origin-destination estimation using probe vehicle trajectory and link counts. J. Adv. Transp. 18, 1–27 (2017). https://doi.org/10.1155/2017/4341532

    Article  Google Scholar 

  • Zhou, X., Mahmassani, H.S.: Dynamic origin–destination demand estimation using automatic vehicle identification data. IEEE Trans. Intell. Transp. Syst. 7, 105–114 (2006). https://doi.org/10.1109/TITS.2006.869629

    Article  Google Scholar 

  • Van Der Zijpp, N.: Dynamic origin–destination matrix estimation from traffic counts and automated vehicle identification data. Transp. Res. Rec. J. Transp. Res. Board. 1607, 87–94 (1997). https://doi.org/10.3141/1607-13

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank PTV group for providing PTV VISUM software and anonymous reviewers who helped to improve the paper with their comments and suggestions.

Author information

Authors and Affiliations

  1. Civil Engineering Department, Sharif University of Technology, Tehran, Iran

    Mojtaba Rostami Nasab & Yousef Shafahi

Authors
  1. Mojtaba Rostami Nasab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yousef Shafahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mojtaba Rostami Nasab.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rostami Nasab, M., Shafahi, Y. Estimation of origin–destination matrices using link counts and partial path data. Transportation 47, 2923–2950 (2020). https://doi.org/10.1007/s11116-019-09999-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-019-09999-1

Keywords

Search

Navigation