IDEAS home Printed from https://ideas.repec.org/a/sot/journl/y2011i49p2-23.html
   My bibliography  Save this article

Estimation of origin-destination matrix from traffic counts: the state of the art

Author

Listed:
  • Bera, Sharminda
  • Rao, K. V. Krishna

Abstract

The estimation of up-to-date origin-destination matrix (ODM) from an obsolete trip data, using current available information is essential in transportation planning, traffic management and operations. Researchers from last 2 decades have explored various methods of estimating ODM using traffic count data. There are two categories of ODM; static and dynamic ODM. This paper presents studies on both the issues of static and dynamic ODM estimation, the reliability measures of the estimated matrix and also the issue of determining the set of traffic link count stations required to acquire maximum information to estimate a reliable matrix.

Suggested Citation

  • Bera, Sharminda & Rao, K. V. Krishna, 2011. "Estimation of origin-destination matrix from traffic counts: the state of the art," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 49, pages 2-23.
    • Handle: RePEc:sot:journl:y:2011:i:49:p:2-23
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10077/6182
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Hai & Iida, Yasunori & Sasaki, Tsuna, 1994. "The equilibrium-based origin-destination matrix estimation problem," Transportation Research Part B: Methodological, Elsevier, vol. 28(1), pages 23-33, February.
    2. LeBlanc, Larry J. & Farhangian, Keyvan, 1982. "Selection of a trip table which reproduces observed link flows," Transportation Research Part B: Methodological, Elsevier, vol. 16(2), pages 83-88, April.
    3. Codina, Esteve & Garcia, Ricardo & Marin, Angel, 2006. "New algorithmic alternatives for the O-D matrix adjustment problem on traffic networks," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1484-1500, December.
    4. Maher, M. J., 1983. "Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 17(6), pages 435-447, December.
    5. Lo, H. P. & Zhang, N. & Lam, W. H. K., 1999. "Decomposition algorithm for statistical estimation of OD matrix with random link choice proportions from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 369-385, June.
    6. Yang, Hai & Zhou, Jing, 1998. "Optimal traffic counting locations for origin-destination matrix estimation," Transportation Research Part B: Methodological, Elsevier, vol. 32(2), pages 109-126, February.
    7. Cascetta, Ennio, 1984. "Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 289-299.
    8. Hai Yang & Qiang Meng & Michael G. H. Bell, 2001. "Simultaneous Estimation of the Origin-Destination Matrices and Travel-Cost Coefficient for Congested Networks in a Stochastic User Equilibrium," Transportation Science, INFORMS, vol. 35(2), pages 107-123, May.
    9. Sherali, Hanif D. & Narayanan, Arvind & Sivanandan, R., 2003. "Estimation of origin-destination trip-tables based on a partial set of traffic link volumes," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 815-836, November.
    10. Cascetta, Ennio & Nguyen, Sang, 1988. "A unified framework for estimating or updating origin/destination matrices from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 22(6), pages 437-455, December.
    11. Teodorovic, Dus[caron]an, 1999. "Fuzzy logic systems for transportation engineering: the state of the art," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(5), pages 337-364, June.
    12. Yang, Hai, 1995. "Heuristic algorithms for the bilevel origin-destination matrix estimation problem," Transportation Research Part B: Methodological, Elsevier, vol. 29(4), pages 231-242, August.
    13. Lo, Hing-Po & Chan, Chi-Pak, 2003. "Simultaneous estimation of an origin-destination matrix and link choice proportions using traffic counts," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(9), pages 771-788, November.
    14. Bierlaire, M. & Toint, Ph. L., 1995. "Meuse: An origin-destination matrix estimator that exploits structure," Transportation Research Part B: Methodological, Elsevier, vol. 29(1), pages 47-60, February.
    15. K. Ashok & M. E. Ben-Akiva, 2002. "Estimation and Prediction of Time-Dependent Origin-Destination Flows with a Stochastic Mapping to Path Flows and Link Flows," Transportation Science, INFORMS, vol. 36(2), pages 184-198, May.
    16. Doblas, Javier & Benitez, Francisco G., 2005. "An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 565-591, August.
    17. Yang, Hai & Iida, Yasunori & Sasaki, Tsuna, 1991. "An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 25(5), pages 351-363, October.
    18. Yang, Hai & Sasaki, Tsuna & Iida, Yasunori & Asakura, Yasuo, 1992. "Estimation of origin-destination matrices from link traffic counts on congested networks," Transportation Research Part B: Methodological, Elsevier, vol. 26(6), pages 417-434, December.
    19. Sherali, Hanif D. & Sivanandan, R. & Hobeika, Antoine G., 1994. "A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes," Transportation Research Part B: Methodological, Elsevier, vol. 28(3), pages 213-233, June.
    20. Zhejun Gong, 1998. "Estimating the urban OD matrix: A neural network approach," European Journal of Operational Research, Elsevier, vol. 106(1), pages 108-115, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mathieu Gardrat & Pascal Pluvinet, 2021. "Markov based mesoscopic simulation tool for urban freight: SIMTURB," Working Papers halshs-03284321, HAL.
    2. Sánchez-Díaz, Iván & Holguín-Veras, José & Ban, Xuegang (Jeff), 2015. "A time-dependent freight tour synthesis model," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 144-168.
    3. Malik, Leeza & Tiwari, Geetam & Biswas, Udayin & Woxenius, Johan, 2021. "Estimating urban freight flow using limited data: The case of Delhi, India," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    4. Fusco, G. & Bielli, M. & Cipriani, E. & Gori, S. & Nigro, M., 2013. "Signal settings synchronization and dynamic traffic modelling," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 53, pages 1-7.
    5. Sojung Kim & Marcel Kleiber & Stefan Weber, 2022. "Microscopic Traffic Models, Accidents, and Insurance Losses," Papers 2208.12530, arXiv.org, revised Nov 2023.
    6. S. Travis Waller & Sai Chand & Aleksa Zlojutro & Divya Nair & Chence Niu & Jason Wang & Xiang Zhang & Vinayak V. Dixit, 2021. "Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    7. Yang, Yudi & Fan, Yueyue, 2015. "Data dependent input control for origin–destination demand estimation using observability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 385-403.
    8. Alex A. Kurzhanskiy, 2022. "A Methodology for Estimating Vehicle Route Choice from Sparse Flow Measurements in a Traffic Network," Mathematics, MDPI, vol. 10(3), pages 1-11, February.
    9. Mojtaba Rostami Nasab & Yousef Shafahi, 2020. "Estimation of origin–destination matrices using link counts and partial path data," Transportation, Springer, vol. 47(6), pages 2923-2950, December.
    10. Abderrahman Ait-Ali & Jonas Eliasson, 2022. "The value of additional data for public transport origin–destination matrix estimation," Public Transport, Springer, vol. 14(2), pages 419-439, June.
    11. Owais, Mahmoud & Moussa, Ghada S. & Hussain, Khaled F., 2019. "Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach," Operations Research Perspectives, Elsevier, vol. 6(C).
    12. Louis Grange & Felipe González & Shlomo Bekhor, 2017. "Path Flow and Trip Matrix Estimation Using Link Flow Density," Networks and Spatial Economics, Springer, vol. 17(1), pages 173-195, March.
    13. Ghafelebashi, Ali & Razaviyayn, Meisam & Dessouky, Maged, 2021. "Congestion Reduction via Personalized Incentives," Institute of Transportation Studies, Working Paper Series qt5b82168n, Institute of Transportation Studies, UC Davis.
    14. Guido Gentile & Daniele Vigo, 2013. "Movement generation and trip distribution for freight demand modelling applied to city logistics," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 54, pages 1-6.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hai Yang & Qiang Meng & Michael G. H. Bell, 2001. "Simultaneous Estimation of the Origin-Destination Matrices and Travel-Cost Coefficient for Congested Networks in a Stochastic User Equilibrium," Transportation Science, INFORMS, vol. 35(2), pages 107-123, May.
    2. Louis Grange & Felipe González & Shlomo Bekhor, 2017. "Path Flow and Trip Matrix Estimation Using Link Flow Density," Networks and Spatial Economics, Springer, vol. 17(1), pages 173-195, March.
    3. Simonelli, Fulvio & Marzano, Vittorio & Papola, Andrea & Vitiello, Iolanda, 2012. "A network sensor location procedure accounting for o–d matrix estimate variability," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1624-1638.
    4. Yang, Yudi & Fan, Yueyue, 2015. "Data dependent input control for origin–destination demand estimation using observability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 385-403.
    5. Tao Li, 2017. "A Demand Estimator Based on a Nested Logit Model," Transportation Science, INFORMS, vol. 51(3), pages 918-930, August.
    6. Lundgren, Jan T. & Peterson, Anders, 2008. "A heuristic for the bilevel origin-destination-matrix estimation problem," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 339-354, May.
    7. Sherali, Hanif D. & Narayanan, Arvind & Sivanandan, R., 2003. "Estimation of origin-destination trip-tables based on a partial set of traffic link volumes," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 815-836, November.
    8. Xie, Chi & Kockelman, Kara M. & Waller, S. Travis, 2011. "A maximum entropy-least squares estimator for elastic origin–destination trip matrix estimation," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1465-1482.
    9. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    10. Yang, Yudi & Fan, Yueyue & Wets, Roger J.B., 2018. "Stochastic travel demand estimation: Improving network identifiability using multi-day observation sets," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 192-211.
    11. T. Abrahamsson, 1998. "Estimation of Origin-Destination Matrices Using Traffic Counts- A Literature Survey," Working Papers ir98021, International Institute for Applied Systems Analysis.
    12. Abdullah Alshehri & Mahmoud Owais & Jayadev Gyani & Mishal H. Aljarbou & Saleh Alsulamy, 2023. "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    13. Doblas, Javier & Benitez, Francisco G., 2005. "An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 565-591, August.
    14. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar, 2008. "Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 455-481, June.
    15. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.
    16. Enrique Castillo & Pilar Jiménez & José Menéndez & María Nogal, 2013. "A Bayesian method for estimating traffic flows based on plate scanning," Transportation, Springer, vol. 40(1), pages 173-201, January.
    17. Shen, Wei & Wynter, Laura, 2012. "A new one-level convex optimization approach for estimating origin–destination demand," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1535-1555.
    18. Menon, Aditya Krishna & Cai, Chen & Wang, Weihong & Wen, Tao & Chen, Fang, 2015. "Fine-grained OD estimation with automated zoning and sparsity regularisation," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 150-172.
    19. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    20. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sot:journl:y:2011:i:49:p:2-23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Romeo Danielis (email available below). General contact details of provider: https://edirc.repec.org/data/xxxxxxx.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.