Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem
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ML can help solve the bike sharing rebalancing problem by improving forecasts of bike demand and inventory.
“ML can also help solve the bike sharing rebalancing problem, where shared bikes accumulate in one location and are lacking in other locations, by improving forecasts of bike demand and inventory [675].”
From Tackling climate change with machine learning (2023) · cites this paper as [675]
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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
- Regue, R., & Recker, W.
- Affiliations
- University of California, Irvine (US)
- Venue
- Transportation Research Part E: Logistics and Transportation Review
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 72
- Pages
- 192-209
- ISSN
- 1366-5545 (ISSN-L)
- OpenAlex
- W1984698666
- Cited by
- 148
- References
- 30
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 16 · 2023: 13 · 2022: 23 · 2021: 22 · 2020: 13 · 2019: 18 · 2018: 13 · 2017: 3 · 2016: 9
- FWCI
- 8.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Part E Logistics and Transportation Review (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Generalitat de Catalunya
- Fundación Caja Madrid
- Topics
- Social Sciences › Transportation › Urban Transport and Accessibility
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Vehicle Routing Optimization Methods
- Keywords
- bike sharing, bike rebalancing, vehicle routing, demand forecasting, station inventory, dynamic demand, peak load
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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.
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