arXiv 4 Apr 2022 · Econometrics · publishedTransportation Research Part C Emerging Technologies (2023) · 21 citations (OpenAlex)
arXiv:2204.01215 · PDF · DOI · OpenAlex · Extracted main text
Although the recursive logit (RL) model has been recently popular and has led to many applications and extensions, an important numerical issue with respect to the computation of value functions remains unsolved. This issue is particularly significant for model estimation, during which the parameters are updated every iteration and may violate the feasibility condition of the value function. To solve this numerical issue of the value function in the model estimation, this study performs an extensive analysis of a prism-constrained RL (Prism-RL) model proposed by Oyama and Hato (2019), which has a path set constrained by the prism defined based upon a state-extended network representation. The numerical experiments have shown two important properties of the Prism-RL model for parameter estimation. First, the prism-based approach enables estimation regardless of the initial and true parameter values, even in cases where the original RL model cannot be estimated due to the numerical problem. We also successfully captured a positive effect of the presence of street green on pedestrian route choice in a real application. Second, the Prism-RL model achieved better fit and prediction performance than the RL model, by implicitly restricting paths with large detour or many loops. Defining the prism-based path set in a data-oriented manner, we demonstrated the possibility of the Prism-RL model describing more realistic route choice behavior. The capture of positive network attributes while retaining the diversity of path alternatives is important in many applications such as pedestrian route choice and sequential destination choice behavior, and thus the prism-based approach significantly extends the practical applicability of the RL model.
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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 | Oyama, Y., Hato, E (2019) Prism-based path set restriction for solving Markovian traffic assignment problem self | 1.000 | 12 | 6 | 100% |
| 2 | Fosgerau, M., Frejinger, E., Karlstrom, A (2013) A link based network route choice model with unrestricted choice set | 1.000 | 10 | 4 | 100% |
| 3 | Mai, T., Fosgerau, M., Frejinger, E (2015) A nested recursive logit model for route choice analysis | 0.969 | 11 | 5 | 91% |
| 4 | Mai, T., Frejinger, E (2022) Undiscounted recursive path choice models: Convergence properties and algorithms | 0.928 | 5 | 3 | 80% |
| 5 | Oyama, Y., Hara, Y., Akamatsu, T (2022) Markovian traffic equilibrium assignment based on network generalized extreme value model self | 0.928 | 4 | 3 | 100% |
| 6 | Oyama, Y., Hato, E (2017) A discounted recursive logit model for dynamic gridlock network analysis self | 0.811 | 4 | 2 | 100% |
| 7 | Rust, J (1987) Optimal replacement of gmc bus engines: An empirical model of harold zurcher | 0.737 | 3 | 2 | 100% |
| 8 | Akamatsu, T (1996) Cyclic flows, Markov process and stochastic traffic assignment | 0.644 | 4 | 1 | 100% |
| 9 | Mai, T (2016) A method of integrating correlation structures for a generalized recursive route choice model | 0.644 | 2 | 2 | 100% |
| 10 | Basu, R., Sevtsuk, A (2022) How do street attributes affect willingness-to-walk? City-wide pedestrian route choice analysis using big data from Boston and S… | 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 | Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of locally perceived attributes | 0.874 | 5 | 2 |
| 2 | Constrained Recursive Logit for Route Choice Analysis | 0.405 | 1 | 1 |