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Subnetwork Origin-Destination Matrix Estimation Under Travel Demand Constraints

Author

Listed:
  • Chao Sun

    (Jiangsu University
    Southeast University)

  • Yulin Chang

    (Jiangsu University
    University of Southampton)

  • Yuji Shi

    (Jiangsu University)

  • Lin Cheng

    (Southeast University)

  • Jie Ma

    (Southeast University)

Abstract

This paper proposes a subnetwork origin-destination (OD) matrix estimation model under travel demand constraints (SME-DC) that explicitly considers both internal-external subnetwork connections and OD demand consistency between the subnetwork and full network. This new model uses the maximum entropy of OD demands as the objective function and uses the total traffic generations (attractions) along with some fixed OD demands of the subnetwork OD nodes as the constraints. The total traffic generations and attractions along with the fixed OD demands of the subnetwork OD nodes are obtained through an OD node transformation and subnetwork topology analysis. For solving the proposed model, a convex combination method is used to convert the nonlinear SME-DC to the classical linear transportation problem, and a tabular method is used to solve the transportation problem. The Sioux Falls network and Kunshan network were provided to illustrate the essential ideas of the proposed model and the applicability of the proposed solution algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:netspa:v:19:y:2019:i:4:d:10.1007_s11067-019-09449-6
    DOI: 10.1007/s11067-019-09449-6
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    References listed on IDEAS

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    1. 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.

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