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Stochastic OD demand estimation using stochastic programming

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  • Sun, Ran
  • Fan, Yueyue

Abstract

Understanding the origin–destination (OD) demand of travelers can help traffic operators and mobility service providers form more efficient mobility planning and operation decisions. Large quantities of high-dimensional spatial and temporal data that are becoming increasingly available for urban transportation systems present opportunities as well as new challenges to this end. Approaching from a fresh angle of stochastic programming, we present a modeling framework for OD demand estimation based on observed traffic flow data in a transportation network. The proposed two-stage stochastic programming-based method is flexible to incorporate various design principles and risk preferences and domain knowledge regarding travel behavioral and physical rules. Additionally, a benefit comes from the scenario representation, where the point estimate can be combined with estimation of the discrete approximation to the demand distribution. As a result, we simultaneously incorporate demand parameter estimation and trip table reconstruction processes. In addition, we demonstrate that under the proposed framework, well-established theories and methods for stochastic programming, including epi-convergence and scenario-decomposition, can be exploited to advance the analytical and computational capabilities of the estimation model. The applicability and efficiency of the proposed method are illustrated through numerical examples based on highway and transit networks of various sizes.

Suggested Citation

  • Sun, Ran & Fan, Yueyue, 2024. "Stochastic OD demand estimation using stochastic programming," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:transb:v:183:y:2024:i:c:s0191261524000675
    DOI: 10.1016/j.trb.2024.102943
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    References listed on IDEAS

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    1. Yong Wang & Xiaolei Ma & Yong Liu & Ke Gong & Kristian C Henricakson & Maozeng Xu & Yinhai Wang, 2016. "A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-24, January.
    2. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    3. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    4. Yueyue Fan & Changzheng Liu, 2010. "Solving Stochastic Transportation Network Protection Problems Using the Progressive Hedging-based Method," Networks and Spatial Economics, Springer, vol. 10(2), pages 193-208, June.
    5. Jean-Paul Watson & Roger J-B Wets & David L. Woodruff, 2010. "Scalable Heuristics for a Class of Chance-Constrained Stochastic Programs," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 543-554, November.
    6. Lisa A. Korf & Roger J.-B. Wets, 2001. "Random LSC Functions: An Ergodic Theorem," Mathematics of Operations Research, INFORMS, vol. 26(2), pages 421-445, 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. Yang, Yudi & Fan, Yueyue & Royset, Johannes O., 2019. "Estimating probability distributions of travel demand on a congested network," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 265-286.
    9. 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.
    10. Wolfram Wiesemann & Daniel Kuhn & Melvyn Sim, 2014. "Distributionally Robust Convex Optimization," Operations Research, INFORMS, vol. 62(6), pages 1358-1376, December.
    11. Alan J. King & R. Tyrrell Rockafellar, 1993. "Asymptotic Theory for Solutions in Statistical Estimation and Stochastic Programming," Mathematics of Operations Research, INFORMS, vol. 18(1), pages 148-162, February.
    12. 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.
    13. Joaquín de Cea & Enrique Fernández, 1993. "Transit Assignment for Congested Public Transport Systems: An Equilibrium Model," Transportation Science, INFORMS, vol. 27(2), pages 133-147, May.
    14. Hazelton, Martin L., 2008. "Statistical inference for time varying origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 542-552, July.
    15. Nguyen, S. & Morello, E. & Pallottino, S., 1988. "Discrete time dynamic estimation model for passenger origin/destination matrices on transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 22(4), pages 251-260, August.
    16. Wong, S. C. & Tong, C. O., 1998. "Estimation of time-dependent origin-destination matrices for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 32(1), pages 35-48, January.
    17. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    18. Lo, H. P. & Zhang, N. & Lam, W. H. K., 1996. "Estimation of an origin-destination matrix with random link choice proportions: A statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 30(4), pages 309-324, August.
    19. Ricardo Collado & Dávid Papp & Andrzej Ruszczyński, 2012. "Scenario decomposition of risk-averse multistage stochastic programming problems," Annals of Operations Research, Springer, vol. 200(1), pages 147-170, November.
    20. 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.
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