2026-09-18T21:37:18Zhttps://ecommons.cornell.edu/server/oai/requestoai:ecommons.cornell.edu:1813/340702026-05-14T13:57:00Zcom_1813_35col_1813_47
Zhao, Qing
Turnquist, Mark Alan
Gao, Huaizhu
Topaloglu, Huseyin
2013-09-05T15:57:03Z
2018-05-27T06:00:31Z
2013-05-26
https://hdl.handle.net/1813/34070
8267567
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.
en_US
OD estimation
Multiclass
Multiple data
Multiclass Origin-Destination Estimation Using Multiple Data Types
dissertation or thesis
application/pdf
Civil and Environmental Engineering
Cornell University
Master of Science
M.S., Civil and Environmental Engineering
Publication
Cornell Theses and Dissertations
Zhao, Qing
5893a6ea-7af3-41d7-abc6-04bcd26ab5df
open.access
open.access
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<Abstract>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.</Abstract>
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