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Dynamic ride-sharing: A simulation study in metro Atlanta

Agatz, Niels A.H., Erera, Alan L., Savelsbergh, Martin W.P., Wang, Xing·2011·Transportation Research Part B: Methodological

Abstract

Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles incurred by system users, and their individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers.

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Metadata

Authors
Agatz, N. A. H., Erera, A. L., Savelsbergh, M. W. P., & Wang, X.
Venue
Transportation Research Part B: Methodological
Publisher
Elsevier BV
Published
2011-11
Volume
45
Issue
9
Pages
1450-1464
ISSN
0191-2615 (ISSN-L)
OpenAlex
W1982002004
Cited by
475
References
0
FWCI
21.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Dynamic ride-sharing: A simulation study in metro Atlanta. https://app.cochat.ai/papers/10.1016/j.trb.2011.05.017
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Agatz, N. A. H., Erera, A. L., Savelsbergh, M. W. P., & Wang, X. (2011). Dynamic ride-sharing: A simulation study in metro Atlanta. Transportation Research Part B: Methodological. https://doi.org/10.1016/j.trb.2011.05.017

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.