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

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.

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  • English

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  • Accession Number: 01532601
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 24 2014 9:29AM