Multiclass Origin-Destination Estimation Using Multiple Data Types

Estimating O-D tables for trucks is of substantial interest due to different emission characteristics, pavement damage, etc of trucks. This thesis proposes a bilevel optimization model and corresponding solution method for static multi-class O-D estimation using various types of data. Limited memory BFGS method with bounded constraints is used for solving the upper level optimization, which is used to derive O-D table entries by minimizing the sum of squared differences between observations from different data sources and the predictions of those values. A probit model is assumed in the lower-level stochastic user equilibrium problem for flow prediction. Extensive experiments have been performed on a test network with different types of link count sensors and turning movements. The tests verify the problem formulation and solution algorithm, and offer important insights into the multiclass O-D estimation process with different types of data available.

Name thesis:degree_name

M.S., Civil and Environmental Engineering

Level thesis:degree_level

Master of Science

Discipline thesis:degree_discipline

Civil and Environmental Engineering

Grantor

Cornell University

Year dc:date.issued

2013

Author dc:creator

• Zhao, Qing

Committee members dc:contributor.committeemember

• Gao, Huaizhu
• Topaloglu, Huseyin

• OD estimation
• Multiclass
• Multiple data

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1813/34070

OAI identifier oai:identifier

oai:ecommons.cornell.edu:1813/34070