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On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment
Open accessAbstract
Significance: Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min.
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Metadata
- Authors
- Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., & Rus, D.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2017-01-03
- Volume
- 114
- Issue
- 3
- Pages
- 462-467
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2565919573
- Cited by
- 1,186
- References
- 1
- FWCI
- 67.98
- Type
- journal article
- Language
- en
- Open access
- bronze
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
