arXiv 7 Jun 2023 · Econometrics · publishedTransportation Research Part B Methodological (2024) · 2 citations (OpenAlex)
arXiv:2306.04606 · PDF · DOI · OpenAlex · Extracted main text
In many choice modeling applications, people demand is frequently characterized as multiple discrete, which means that people choose multiple items simultaneously. The analysis and prediction of people behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such that the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.
appendix boundary found by appendix_command · 80% of the source is main text. Read the extracted text to check this.
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 | Mai, T., Fosgerau, M., and Frejinger, E (2015) A nested recursive logit model for route choice analysis self | 1.000 | 11 | 6 | 100% |
| 2 | Fosgerau, M., Frejinger, E., and Karlström, A (2013) A link based network route choice model with unrestricted choice set | 1.000 | 6 | 4 | 100% |
| 3 | Mai, T. and Frejinger, E (2022) Undiscounted recursive path choice models: Convergence properties and algorithms self | 0.928 | 4 | 3 | 100% |
| 4 | McFadden, D (1981) Econometric models of probabilistic choice | 0.843 | 3 | 3 | 100% |
| 5 | Prato, C. G (2009) Route choice modeling: past, present and future research directions | 0.644 | 2 | 2 | 100% |
| 6 | Bhat, C. R (2008) The multiple discrete-continuous extreme value (mdcev) model: role of utility function parameters, identification considerations… | 0.644 | 2 | 2 | 100% |
| 7 | Bhat, C. R., Castro, M., and Pinjari, A. R (2015) Allowing for complementarity and rich substitution patterns in multiple discrete–continuous models | 0.644 | 2 | 2 | 100% |
| 8 | Bhat, C. R (2022) A closed-form multiple discrete-count extreme value (mdcntev) model | 0.644 | 2 | 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., Yu, X., Gao, S., and Frejinger, E (2021) Route choice in a stochastic time-dependent network: the recursive model and solution algorithm self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Equilibrium-Constrained Estimation of Recursive Logit Choice Models | 1.000 | 9 | 3 |