| Article ID: | iaor20141958 |
| Volume: | 67 |
| Issue: | 11 |
| Start Page Number: | 71 |
| End Page Number: | 91 |
| Publication Date: | Jul 2014 |
| Journal: | Transportation Research Part E |
| Authors: | Chow Joseph Y J, Ritchie Stephen G, Jeong Kyungsoo |
| Keywords: | allocation: resources, combinatorial optimization, programming: nonlinear, networks: flow |
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.