Optimal Operations Management of Mobility-on-Demand Systems
Abstract
The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet size yield utilization of the vehicles in the fleet is around 75% compared to private vehicle utilization of 5%.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 08, 2021
- Source ID
- 10.3389/frsc.2021.681096
Entities
People
- Christos G. Cassandras
- Ioannis Ch. Paschalidis
- Salomon Wollenstein-Betech
Organizations
- ARPA-E
- Air Force Office of Scientific Research
- Division of Civil, Mechanical & Manufacturing Innovation
- Division of Computer and Network Systems
- Division of Electrical, Communications & Cyber Systems
- Division of Information and Intelligent Systems
- MathWorks
- National Institutes of Health
- National Science Foundation Division of Mathematical Sciences
- Office of Naval Research