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Inferring origin-destination trip matrices with a decoupled GLS path flow estimator

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  • Nie, Yu
  • Zhang, H.M.
  • Recker, W.W.

Abstract

Recently, path flow estimators (PFE) have been used for the estimation of origin-destination (O-D) matrices. This paper develops a formulation that incorporates a decoupled path flow estimator in a generalized least squares (GLS) framework. The approach seeks to solve a GLS problem that minimizes the sum of errors in traffic counts and O-D matrices based on an equilibrium assignment mapping derived exogenously from a K-shortest path ranking procedure. Solving the GLS-PFE inevitably involves non-invertible linear systems and non-negative constraints. A solution algorithm is designed to iteratively identify active constraints and solve linear systems by computing the pseudoinverse. A simplified version of this algorithm is further developed to improve its computational efficiency. The solution properties and computational efficiency of the two methods are tested and compared for small to mid-size networks. It is concluded that the simplified algorithm is efficient in solving the decoupled GLS-PFE problem for realistic size networks.

Suggested Citation

  • Nie, Yu & Zhang, H.M. & Recker, W.W., 2005. "Inferring origin-destination trip matrices with a decoupled GLS path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 39(6), pages 497-518, July.
    • Handle: RePEc:eee:transb:v:39:y:2005:i:6:p:497-518
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Tao Li, 2017. "A Demand Estimator Based on a Nested Logit Model," Transportation Science, INFORMS, vol. 51(3), pages 918-930, August.
    3. 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.
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    5. Kumar, Anshuman Anjani & Kang, Jee Eun & Kwon, Changhyun & Nikolaev, Alexander, 2016. "Inferring origin-destination pairs and utility-based travel preferences of shared mobility system users in a multi-modal environment," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 270-291.
    6. 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.
    7. Rindt, Craig R. & McNally, Michael G., 2007. "Field Deployment and Operational Test of an Agent-based, Multi-Jurisdictional Traffic Management System," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0nd2p0k4, Institute of Transportation Studies, UC Berkeley.
    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. Yu Nie & H. Zhang, 2010. "A Relaxation Approach for Estimating Origin–Destination Trip Tables," Networks and Spatial Economics, Springer, vol. 10(1), pages 147-172, March.
    10. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    11. 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.
    12. 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.
    13. 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.
    14. Rindt, Craig R. & McNally, Michael G., 2009. "Cartesius and CTNET Integration and Field Operational Test," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1qn7q6zf, Institute of Transportation Studies, UC Berkeley.
    15. Hazelton, Martin L., 2010. "Bayesian inference for network-based models with a linear inverse structure," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 674-685, June.
    16. 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.
    17. Ballis, Haris & Dimitriou, Loukas, 2020. "Revealing personal activities schedules from synthesizing multi-period origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 224-258.
    18. Li, Tao & Wan, Yan, 2019. "Estimating the geographic distribution of originating air travel demand using a bi-level optimization model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 267-291.
    19. Maryam Abareshi & Mehdi Zaferanieh & Bagher Keramati, 2017. "Path Flow Estimator in an Entropy Model Using a Nonlinear L-Shaped Algorithm," Networks and Spatial Economics, Springer, vol. 17(1), pages 293-315, March.

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