arXiv 14 Jul 2023 · physics.soc-ph · publishedTransportation Research Part A Policy and Practice (2024) · 9 citations (OpenAlex)
arXiv:2307.08646 · PDF · DOI · OpenAlex · Extracted main text
This study performs an attribute-level analysis of the global and local path preferences of network travelers. To this end, a reward decomposition approach is proposed and integrated into a link-based recursive (Markovian) path choice model. The approach decomposes the instantaneous reward function associated with each state-action pair into the global utility, a function of attributes globally perceived from anywhere in the network, and the local utility, a function of attributes that are only locally perceived from the current state. Only the global utility then enters the value function of each state, representing the future expected utility toward the destination. This global-local path choice model with decomposed reward functions allows us to analyze to what extent and which attributes affect the global and local path choices of agents. Moreover, unlike most adaptive path choice models, the proposed model can be estimated based on revealed path observations (without the information of plans) and as efficiently as deterministic recursive path choice models. The model was applied to the real pedestrian path choice observations in an urban street network where the green view index was extracted as a visual street quality from Google Street View images. The result revealed that pedestrians locally perceive and react to the visual street quality, rather than they have the pre-trip global perception on it. Furthermore, the simulation results using the estimated models suggested the importance of location selection of interventions when policy-related attributes are only locally perceived by travelers.
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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., Karlstrom, A (2013) A link based network route choice model with unrestricted choice set | 1.000 | 12 | 5 | 100% |
| 2 | Oyama, Y., Hato, E (2017) A discounted recursive logit model for dynamic gridlock network analysis self | 0.956 | 8 | 5 | 88% |
| 3 | Oyama, Y (2023) Capturing positive network attributes during the estimation of recursive logit models: A prism-based approach self | 0.874 | 5 | 2 | 100% |
| 4 | Rust, J (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher | 0.843 | 3 | 3 | 100% |
| 5 | Como, G., Savla, K., Acemoglu, D., Dahleh, M.A., Frazzoli, E (2013) Stability analysis of transportation networks with multiscale driver decisions | 0.843 | 3 | 3 | 100% |
| 6 | Gao, S., Frejinger, E., Ben-Akiva, M (2010) Adaptive route choices in risky traffic networks: A prospect theory approach | 0.843 | 3 | 3 | 100% |
| 7 | Zhao, Z., Liang, Y (2023) A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards | 0.843 | 3 | 3 | 100% |
| 8 | Ziebart, B.D., Maas, A.L., Bagnell, J.A., Dey, A.K., et al (2008) Maximum entropy inverse reinforcement learning | 0.843 | 3 | 3 | 100% |
| 9 | Mai, T., Fosgerau, M., Frejinger, E (2015) A nested recursive logit model for route choice analysis | 0.737 | 3 | 2 | 100% |
| 10 | Abdel-Aty, M.A., Kitamura, R., Jovanis, P.P (1997) Using stated preference data for studying the effect of advanced traffic information on drivers' route choice | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 45 scored citations.