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An Excess-Demand Dynamic Traffic Assignment Approach for Inferring Origin-Destination Trip Matrices

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  • Chi Xie
  • Jennifer Duthie

Abstract

The focus of this paper is on the development of an origin–destination (O-D) demand estimation method for dynamic equilibrium traffic networks. It is hypothesized that the underlying equilibrium conditions in such networks are a compromise result of minimization of individual routing costs, minimization of traffic count matching errors, and maximization of O-D demand entropies. By adding an upper bound of travel demand and a dummy path with constant travel cost to each O-D pair, we formulated the dynamic O-D demand estimation problem as an excess-demand dynamic traffic assignment (DTA) problem defined for an expanded network with dummy paths. Such a formulation enables us to apply existing DTA solution methods and software tools for deriving the path flow pattern in the expanded network and thus simultaneously obtaining the O-D demand pattern in the original network. Following this problem transformation and network expansion strategy, an iterative solution procedure is accordingly proposed, which resorts to repeatedly solving the excess-demand DTA problem and adjusting the dummy path costs. An application of the proposed modeling and solution methods for an example cell-based network problem favorably illustrates great promise of the methodological advance and solution performance. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Chi Xie & Jennifer Duthie, 2015. "An Excess-Demand Dynamic Traffic Assignment Approach for Inferring Origin-Destination Trip Matrices," Networks and Spatial Economics, Springer, vol. 15(4), pages 947-979, December.
    • Handle: RePEc:kap:netspa:v:15:y:2015:i:4:p:947-979
    DOI: 10.1007/s11067-014-9277-z
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Chao Sun & Yulin Chang & Xin Luan & Qiang Tu & Wenyun Tang, 2020. "Origin-Destination Demand Reconstruction Using Observed Travel Time under Congested Network," Networks and Spatial Economics, Springer, vol. 20(3), pages 733-755, September.
    3. Chao Sun & Yulin Chang & Yuji Shi & Lin Cheng & Jie Ma, 2019. "Subnetwork Origin-Destination Matrix Estimation Under Travel Demand Constraints," Networks and Spatial Economics, Springer, vol. 19(4), pages 1123-1142, December.
    4. 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.
    5. Vo, Khoa D. & Lam, William H.K. & Chen, Anthony & Shao, Hu, 2020. "A household optimum utility approach for modeling joint activity-travel choices in congested road networks," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 93-125.
    6. 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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