Multiclass Probit-Based Origin–Destination Estimation Using Multiple Data Types
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 144, Issue 6
Abstract
This paper proposes a bilevel optimization model for multiclass origin–destination (O–D) estimation using various types of data. The multiclass character of the model, a new feature and major contribution to the literature, is important because of increasing interest in simultaneous estimation of O–D tables for various classes of trucks and automobiles. The upper-level optimization 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 the different types of available data. Adding turning movement data can improve O–D estimation by 71%. Furthermore, classification information is interchangeable among different types of sensors.
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Acknowledgments
This research was supported in part by Xerox Corporation and the U.S. Department of Transportation through the Region II University Transportation Research Center. This support is gratefully acknowledged, but the authors are solely responsible for the content and findings of the paper.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 144 • Issue 6 • June 2018
Copyright
This work is made available under the terms of the Creative Commons Attribution 4.0 International license, http://creativecommons.org/licenses/by/4.0/.
History
Received: Jun 13, 2017
Accepted: Oct 20, 2017
Published online: Mar 27, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 27, 2018
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