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

Joseph Y.J. Chow; Stephen G. Ritchie; Kyungsoo Jeong

doi:10.1016/j.tre.2014.04.004

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

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

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	71-91
Number of pages	21
Journal	Transportation Research Part E: Logistics and Transportation Review
Volume	67
DOIs	https://doi.org/10.1016/j.tre.2014.04.004
State	Published - Jul 2014

Keywords

Freight forecast
Inverse optimization
Network assignment
Nonlinear optimization
Transshipment

ASJC Scopus subject areas

Business and International Management
Civil and Structural Engineering
Transportation

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10.1016/j.tre.2014.04.004

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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.",

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author = "Chow, {Joseph Y.J.} and Ritchie, {Stephen G.} and Kyungsoo Jeong",

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language = "English (US)",

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AU - Jeong, Kyungsoo

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AB - 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.

KW - Freight forecast

KW - Inverse optimization

KW - Network assignment

KW - Nonlinear optimization

KW - Transshipment

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JO - Transportation Research Part E: Logistics and Transportation Review

JF - Transportation Research Part E: Logistics and Transportation Review

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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