Publication Type

Journal Article

Version

submittedVersion

Publication Date

7-2023

Abstract

This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a new estimation method based on the idea that the choice probabilities of unconnected link observations can be exactly computed by solving systems of linear equations. We further design a new algorithm, called decomposition–composition (DC), that helps reduce the number of systems of linear equations to be solved and speed up the estimation. We compare our proposed algorithms with some standard baselines using a dataset from a real network, and show that the DC algorithm outperforms the other approaches in recovering missing information in the observations. Our methods work with most of the recursive route choice models proposed in the literature, including the recursive logit, nested recursive logit, or discounted recursive models.

Keywords

Decomposition-composition, Expectation-maximization, Incomplete observations, Nested recursive logit, Recursive logit

Discipline

Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Transportation Research Part B: Methodological

Volume

173

First Page

313

Last Page

331

ISSN

0191-2615

Identifier

10.1016/j.trb.2023.05.004

Publisher

Elsevier

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1016/j.trb.2023.05.004

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