The evaluation of the spatial integration of station areas via the node place model; an application to subway station areas in Tehran
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ML-based analysis has shown that the development of efficient public transportation is dependent on both the extent of urban sprawl and the local development around transportation hubs.
“ML-based analysis has shown that the development of efficient public transportation is dependent on both the extent of urban sprawl and the local development around transportation hubs [547,737].”
From Tackling climate change with machine learning (2023) · cites this paper as [547]
Cites
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Monajem, S., & Ekram Nosratian, F.
- Venue
- Transportation Research Part D: Transport and Environment
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 40
- Pages
- 14-27
- ISSN
- 1361-9209 (ISSN-L)
- OpenAlex
- W1766676081
- Cited by
- 95
- References
- 26
- Citations by year
- 2026: 4 · 2025: 16 · 2024: 16 · 2023: 11 · 2022: 15 · 2021: 13 · 2020: 7 · 2019: 6 · 2018: 5 · 2017: 1 · 2016: 1
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Part D Transport and Environment (publishedVersion)
- Topics
- Social Sciences › Transportation › Urban Transport and Accessibility
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- node-place model, land use and transportation integration, spatial integration, spatial indices, street connectivity, pedestrian movement, accessibility (transport and spatial geography), station area development
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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