IDEAS home Printed from https://ideas.repec.org/a/inm/ortrsc/v36y2002i2p184-198.html
   My bibliography  Save this article

Estimation and Prediction of Time-Dependent Origin-Destination Flows with a Stochastic Mapping to Path Flows and Link Flows

Author

Listed:
  • K. Ashok

    (Marketing and Planning Systems, 1100 Winter Street, Waltham, Massachusetts 02451)

  • M. E. Ben-Akiva

    (Massachusetts Institute of Technology, Room 1-181, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139)

Abstract

This paper presents a new suite of models for the estimation and prediction of time-dependent Origin-Destination (O-D) matrices. The key contribution of the proposed approach is the explicit modeling and estimation of the dynamic mapping (the assignment matrix) between time-dependent O-D flows and link volumes. The assignment matrix depends upon underlying travel times and route choice fractions in the network. Since the travel times and route choice fractions are not known with certainty, the assignment matrix is prone to error. The proposed approach provides a systematic way of modeling this uncertainty to address both the offline and real-time versions of the O-D estimation/prediction problem. Preliminary empirical results indicate that generalized models with a stochastic assignment matrix could provide better results compared to conventional models with a fixed matrix.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ortrsc:v:36:y:2002:i:2:p:184-198
    DOI: 10.1287/trsc.36.2.184.563
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/trsc.36.2.184.563
    Download Restriction: no
    File URL: https://libkey.io/10.1287/trsc.36.2.184.563?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cremer, M. & Keller, H., 1987. "A new class of dynamic methods for the identification of origin-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 117-132, April.
    2. Bell, Michael G. H., 1991. "The real time estimation of origin-destination flows in the presence of platoon dispersion," Transportation Research Part B: Methodological, Elsevier, vol. 25(2-3), pages 115-125.
    3. Ennio Cascetta & Domenico Inaudi & Gérald Marquis, 1993. "Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts," Transportation Science, INFORMS, vol. 27(4), pages 363-373, November.
    4. K. Ashok & M. E. Ben-Akiva, 2000. "Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows," Transportation Science, INFORMS, vol. 34(1), pages 21-36, February.
    5. Nihan, Nancy L. & Davis, Gary A., 1987. "Recursive estimation of origin-destination matrices from input/output counts," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 149-163, 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. D'Acierno, Luca & Cartenì, Armando & Montella, Bruno, 2009. "Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System," European Journal of Operational Research, Elsevier, vol. 196(2), pages 719-736, July.
    2. Mohammad Saifuzzaman & Leonid Engelson & Ida Kristoffersson & André de Palma, 2016. "Stockholm congestion charging: an assessment with METROPOLIS and SILVESTER," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(7), pages 653-674, October.
    3. 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.
    4. Cantelmo, Guido & Qurashi, Moeid & Prakash, A. Arun & Antoniou, Constantinos & Viti, Francesco, 2020. "Incorporating trip chaining within online demand estimation," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 171-187.
    5. 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.
    6. 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.
    7. Hsun-Jung Cho & Yow-Jen Jou & Chien-Lun Lan, 2009. "Time Dependent Origin-destination Estimation from Traffic Count without Prior Information," Networks and Spatial Economics, Springer, vol. 9(2), pages 145-170, June.
    8. Lin, Pei-Wei & Chang, Gang-Len, 2007. "A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 554-572, June.
    9. Hu, Shou-Ren & Peeta, Srinivas & Chu, Chun-Hsiao, 2009. "Identification of vehicle sensor locations for link-based network traffic applications," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 873-894, September.
    10. Dimitris Bertsimas & Julia Yan, 2018. "From Physical Properties of Transportation Flows to Demand Estimation: An Optimization Approach," Transportation Science, INFORMS, vol. 52(4), pages 1002-1011, August.
    11. Yong Lin, 2023. "Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    12. Zhang, Chao & Osorio, Carolina & Flötteröd, Gunnar, 2017. "Efficient calibration techniques for large-scale traffic simulators," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 214-239.
    13. Susan Jia Xu & Mehdi Nourinejad & Xuebo Lai & Joseph Y. J. Chow, 2018. "Network Learning via Multiagent Inverse Transportation Problems," Service Science, INFORMS, vol. 52(6), pages 1347-1364, December.
    14. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    15. Shuai, Chunyan & Shan, Jun & Bai, Jincheng & Lee, Jaeyoung & He, Min & Ouyang, Xin, 2022. "Relationship analysis of short-term origin–destination prediction performance and spatiotemporal characteristics in urban rail transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 206-223.

    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. Nie, Yu (Marco) & Zhang, H.M., 2008. "A variational inequality formulation for inferring dynamic origin-destination travel demands," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 635-662, August.
    2. Zhang, Michael & Nie, Yu & Shen, Wei & Lee, Ming S. & Jansuwan, Sarawut & Chootinan, Piya & Pravinvongvuth, Surachet & Chen, Anthony & Recker, Will W., 2008. "Development of A Path Flow Estimator for Inferring Steady-State and Time-Dependent Origin-Destination Trip Matrices," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3nr033sc, Institute of Transportation Studies, UC Berkeley.
    3. K. Ashok & M. E. Ben-Akiva, 2000. "Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows," Transportation Science, INFORMS, vol. 34(1), pages 21-36, February.
    4. Chang, Gang-Len & Tao, Xianding, 1999. "An integrated model for estimating time-varying network origin-destination distributions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(5), pages 381-399, June.
    5. Wu, Jifeng & Chang, Gang-Len, 1996. "Estimation of time-varying origin-destination distributions with dynamic screenline flows," Transportation Research Part B: Methodological, Elsevier, vol. 30(4), pages 277-290, August.
    6. Hjorth, Urban, 2002. "Traffic subflow estimation and bootstrap analysis from filtered counts," Transportation Research Part B: Methodological, Elsevier, vol. 36(4), pages 345-359, May.
    7. Hjorth, U., 1999. "The inherent precision of regression estimated route probabilities," Transportation Research Part B: Methodological, Elsevier, vol. 33(8), pages 593-607, November.
    8. Lin, Pei-Wei & Chang, Gang-Len, 2007. "A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 554-572, June.
    9. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    10. A. Stathopoulos & T. Tsekeris, 2003. "Framework for analysing reliability and information degradation of demand matrices in extended transport networks," Transport Reviews, Taylor & Francis Journals, vol. 23(1), pages 89-103, January.
    11. Zhou, Xuesong & Mahmassani, Hani S., 2007. "A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework," Transportation Research Part B: Methodological, Elsevier, vol. 41(8), pages 823-840, October.
    12. Garcia, Reinaldo C., 2002. "Implementing A Dynamic O-D Estimation Algorithm within the Microscopic Traffic Simulator Paramics," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0n62j6nq, Institute of Transportation Studies, UC Berkeley.
    13. Sun, Carlos & Porwal, Himanshu, 2000. "Dynamic Origin/Destination Estimation Using True Section Densities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0f0711s6, Institute of Transportation Studies, UC Berkeley.
    14. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    15. 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.
    16. A. de Palma & F. Marchal, 2000. "Dynamic traffic analysis with static data: some guidelines with an application to Paris," THEMA Working Papers 2000-55, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    17. Li, Baibing & Moor, Bart De, 2002. "Dynamic identification of origin-destination matrices in the presence of incomplete observations," Transportation Research Part B: Methodological, Elsevier, vol. 36(1), pages 37-57, January.
    18. Li, Baibing & De Moor, Bart, 1999. "Recursive estimation based on the equality-constrained optimization for intersection origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 33(3), pages 203-214, April.
    19. Kumarage, Sakitha & Yildirimoglu, Mehmet & Zheng, Zuduo, 2023. "A hybrid modelling framework for the estimation of dynamic origin–destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    20. Zhang, Xiaoyan & Maher, Mike J., 1998. "The evaluation and application of a fully disaggregate method for trip matrix estimation with platoon dispersion," Transportation Research Part B: Methodological, Elsevier, vol. 32(4), pages 261-276, May.

    More about this item

    Statistics

    Access and download statistics

    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:inm:ortrsc:v:36:y:2002:i:2:p:184-198. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.