On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). The authors present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. The authors quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ~3 million rides extracted from the New York City taxicab public dataset. The authors' experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.

• Record URL:
  https://doi.org/10.1073/pnas.1611675114
• Record URL:
  http://www.pnas.org/content/114/3/462.abstract
• Authors:
  • Alonso-Mora, Javier
  • Samaranayake, Samitha
  • Wallar, Alex
  • Frazzoli, Emilio
  • Rus, Daniela
• Publication Date: 2017-1-17

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 462-467
• Serial:
  • Proceedings of the National Academy of Sciences
  • Volume: 114
  • Issue Number: 3
  • Publisher: National Academy of Sciences
  • EISSN: 1091-6490
  • Serial URL: http://www.pnas.org/

Subject/Index Terms

• TRT Terms: Dynamic traffic assignment; Intelligent transportation systems; Intelligent vehicles; Mathematical models; Mobility; Ridesharing; Vehicle capacity
• Uncontrolled Terms: Smart cities
• Geographic Terms: New York (New York)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01630185
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 27 2017 9:33AM