Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le
arXiv 27 Apr 2022 · Econometrics · publishedTransportation Research Part B Methodological (2023) · 5 citations (OpenAlex)
arXiv:2204.12992 · PDF · DOI · OpenAlex · Extracted main text
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 would 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 to deal 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 would 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 as 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Fosgerau, M., Frejinger, E., and Karlström, A (2013) A link based network route choice model with unrestricted choice set | 1.000 | 20 | 5 | 100% |
| 2 | Mai, T., Fosgerau, M., and Frejinger, E (2015) A nested recursive logit model for route choice analysis self | 1.000 | 18 | 5 | 100% |
| 3 | Dempster, A. P., Laird, N. M., and Rubin, D. B (1977) Maximum likelihood from incomplete data via the em algorithm | 1.000 | 5 | 3 | 100% |
| 4 | Mai, T., Bastin, F., and Frejinger, E (2018) A decomposition method for estimating recursive logit based route choice models self | 0.928 | 4 | 3 | 100% |
| 5 | Mai, T. and Frejinger, E (2022) Estimation of undiscounted recursive path choice models: Convergence properties and algorithms self | 0.843 | 3 | 3 | 100% |
| 6 | Rust, J (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher | 0.737 | 3 | 2 | 100% |
| 7 | Arcidiacono, P. and Miller, R. A (2011) Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity | 0.737 | 3 | 2 | 100% |
| 8 | Mai, T., Yu, X., Gao, S., and Frejinger, E (2021) Route choice in a stochastic time-dependent network: the recursive model and solution algorithm self | 0.737 | 3 | 2 | 100% |
| 9 | de Moraes Ramos, G., Mai, T., Daamen, W., Frejinger, E., and Hoogend… (2020) Route choice behaviour and travel information in a congested network: Static and dynamic recursive models self | 0.644 | 2 | 2 | 100% |
| 10 | Mai, T (2016) A method of integrating correlation structures for a generalized recursive route choice model self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 37 scored citations.