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

• Defined a class of nonlinear inverse optimization problems using KKT conditions.
• Applied method as parameter estimation for freight assignment models.
• Formulated a commodity-vehicle-decoupled variation of STAN model.
• Conducted parameter recovery and sensitivity tests with a small network.
• Estimated airport capacity parameters from prior data and cross-validated them.

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.