On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 03, 2017
- Source ID
- 10.1073/pnas.1611675114
Entities
People
- Alex Wallar
- Daniela L. Rus
- Emilio Frazzoli
- Javier Alonso-Mora
- Samitha Samaranayake
Organizations
- Cornell University
- Massachusetts Institute of Technology
- Office of Naval Research
- Singapore–MIT alliance