Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets
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Higher spatial disaggregation of demand forecast leads to better fleet performance in terms of user wait time and empty fleet miles, though demand forecast quality is decreased at higher disaggregation.
“They conclude that higher the spatial disaggregation of demand forecast, better is the fleet performance in terms of user wait time and empty fleet miles, though the demand forecast quality is decreased at higher disaggregation.”
From Shared autonomous vehicle services: A comprehensive review (2020) · cites this paper as Dandl et al. (2019)
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- Authors
- Dandl, F., Hyland, M., Bogenberger, K., & Mahmassani, H. S.
- Affiliations
- Universität der Bundeswehr München (DE)
- University of California, Irvine (US)
- Northwestern University (US)
- Corresponding author
- Mahmassani, Hani S.
- Venue
- Transportation
- Published
- 2019-05-04
- Volume
- 46
- Issue
- 6
- Pages
- 1975-1996
- ISSN
- 0049-4488 (ISSN-L)
- 1572-9435
- OpenAlex
- W2944404766
- Cited by
- 74
- References
- 36
- Citations by year
- 2026: 12 · 2025: 1 · 2024: 7 · 2023: 15 · 2022: 9 · 2021: 12 · 2020: 16 · 2019: 2
- FWCI
- 4.45
- Type
- journal article
- Language
- en
- Open access
- green
- mediatum.ub.tum.de
- Licence
- Locations
- Transportation (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- Funders
- Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit
- U.S. Department of Transportation
- Northwestern University Transportation Center
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- shared autonomous vehicles, agent-based modeling, taxi data, on-demand mobility services
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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