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.

• Record URL:
  • https://doi.org/10.1016/j.tre.2014.04.004
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1366554514000556
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Chow, Joseph Y J
  • Ritchie, Stephen G
  • Jeong, Kyungsoo
• Publication Date: 2014-7

Language

• English

Media Info

• Media Type: Print
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 71-91
• Serial:
  • Transportation Research Part E: Logistics and Transportation Review
  • Volume: 67
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1366-5545
  • Serial URL: http://www.sciencedirect.com/science/journal/13665545

Subject/Index Terms

• TRT Terms: Commodity flow; Forecasting; Freight transportation; Optimization; Shipping; Traffic assignment
• Uncontrolled Terms: Transshipment
• Subject Areas: Freight Transportation; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01532601
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 31 2014 9:15AM