IDEAS home Printed from https://ideas.repec.org/a/inm/ortrsc/v49y2015i3p559-576.html
   My bibliography  Save this article

The Elevator Trip Origin-Destination Matrix Estimation Problem

Author

Listed:
  • Juha-Matti Kuusinen

    (KONE Corporation, 02150 Espoo, Finland)

  • Janne Sorsa

    (KONE Corporation, 02150 Espoo, Finland)

  • Marja-Liisa Siikonen

    (KONE Corporation, 02150 Espoo, Finland)

Abstract

In this paper, we consider the problem of finding the passenger counts for the origin-destination pairs of a particular single transit route called elevator trip. Assuming that passengers first alight and then board a stopping elevator, we can define an elevator trip as successive stops in one direction of travel with passengers inside the elevator. The elevator trip origin-destination passenger counts, i.e., elevator trip origin-destination matrices, estimated for a given time interval can be combined into a building origin-destination matrix that describes the passenger flow between every pair of floors in the building during that interval. The building origin-destination matrices of successive intervals form traffic statistics that can be used to forecast passenger traffic. The forecasts model the uncertainties related to future passengers, and need to be taken into account in elevator dispatching to make robust dispatching decisions in constantly changing traffic conditions. Many methods exist for estimating an origin-destination matrix for a single transit route such as a bus line. These methods estimate average origin-destination passenger counts from observations made during a given time period on the same route. Because an elevator trip is request driven, there may not be two similar elevator trips even within a day. This means that we need to estimate a separate origin-destination matrix for each elevator trip. A natural requirement then is that the estimated origin-destination passenger counts are integer valued. We formulate the elevator trip origin-destination matrix estimation problem as a box-constrained integer least squares problem, and present branch-and-bound-based algorithms for finding all solutions to the problem. The performance of the algorithms with respect to execution time is studied based on numerical experiments. The results show that the formulation and the algorithms are fast enough for solving elevator trip origin-destination matrix estimation problems in a real elevator group control application.

Suggested Citation

  • Juha-Matti Kuusinen & Janne Sorsa & Marja-Liisa Siikonen, 2015. "The Elevator Trip Origin-Destination Matrix Estimation Problem," Transportation Science, INFORMS, vol. 49(3), pages 559-576, August.
    • Handle: RePEc:inm:ortrsc:v:49:y:2015:i:3:p:559-576
    DOI: 10.1287/trsc.2013.0509
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/trsc.2013.0509
    Download Restriction: no
    File URL: https://libkey.io/10.1287/trsc.2013.0509?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. Li, Yuwei & Cassidy, Michael J., 2007. "A generalized and efficient algorithm for estimating transit route ODs from passenger counts," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 114-125, January.
    2. Li, Baibing, 2009. "Markov models for Bayesian analysis about transit route origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 43(3), pages 301-310, March.
    3. Fisk, C. S., 1988. "On combining maximum entropy trip matrix estimation with user optimal assignment," Transportation Research Part B: Methodological, Elsevier, vol. 22(1), pages 69-73, February.
    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. Li, Yuwei, 2007. "A generalized and efficient algorithm for estimating transit route ODs from passenger counts," University of California Transportation Center, Working Papers qt17m7k4vm, University of California Transportation Center.
    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. 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. 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.
    9. 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.
    10. van Zuylen, Henk J. & Branston, David M., 1982. "Consistent link flow estimation from counts," Transportation Research Part B: Methodological, Elsevier, vol. 16(6), pages 473-476, December.
    11. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
    12. Spiess, Heinz, 1987. "A maximum likelihood model for estimating origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 21(5), pages 395-412, October.
    Full references (including those not matched with items on IDEAS)

    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. Maryam Abareshi & Mehdi Zaferanieh & Mohammad Reza Safi, 2019. "Origin-Destination Matrix Estimation Problem in a Markov Chain Approach," Networks and Spatial Economics, Springer, vol. 19(4), pages 1069-1096, December.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Bielli, Maurizio & Reverberi, Pierfrancesco, 1996. "New operations research and artificial intelligence approaches to traffic engineering problems," European Journal of Operational Research, Elsevier, vol. 92(3), pages 550-572, August.
    9. 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.
    10. 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.
    11. 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.
    12. Hazelton, Martin L., 2000. "Estimation of origin-destination matrices from link flows on uncongested networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(7), pages 549-566, September.
    13. 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.
    14. 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.
    15. 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.
    16. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    17. 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.
    18. T. Abrahamsson, 1998. "Estimation of Origin-Destination Matrices Using Traffic Counts- A Literature Survey," Working Papers ir98021, International Institute for Applied Systems Analysis.
    19. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
    20. 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.

    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:49:y:2015:i:3:p:559-576. 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.