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Nonlinear inverse optimization for parameter estimation of commodity-vehicle-decoupled freight assignment

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  • Chow, Joseph Y.J.
  • Ritchie, Stephen G.
  • Jeong, Kyungsoo

Abstract

A systematic approach to estimate parameters from noisy priors is proposed for traffic assignment problems. It extends inverse optimization theory to nonlinear problems, and defines a new class of parameter estimation problems in the transportation literature for networks under congestion. The approach is used to systematically calibrate a new link-based variation of the STAN model which decouples commodity flows and vehicle flows. The models are tested on a small network and then a case study with real data from California statewide implementation. Cross-validation shows 15% CV of the RMSE.

Suggested Citation

  • Chow, Joseph Y.J. & Ritchie, Stephen G. & Jeong, Kyungsoo, 2014. "Nonlinear inverse optimization for parameter estimation of commodity-vehicle-decoupled freight assignment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 71-91.
    • Handle: RePEc:eee:transe:v:67:y:2014:i:c:p:71-91
    DOI: 10.1016/j.tre.2014.04.004
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    6. Abd Allah A. Mousa & Yousria Abo-Elnaga, 2020. "Stability of Solutions for Parametric Inverse Nonlinear Cost Transportation Problem," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
    7. Li, Xinyan & Xie, Chi & Bao, Zhaoyao, 2022. "A multimodal multicommodity network equilibrium model with service capacity and bottleneck congestion for China-Europe containerized freight flows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

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