Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and
reshaped urban transportation. The theory and technologies behind these platforms have …

In a ride-sharing system, arriving customers must be matched with available drivers. These
decisions affect the overall number of customers matched, because they impact whether …

We address a comprehensive ride-hailing system taking into account many of the decisions
required to operate it in reality. The ride-hailing system is formed of a centrally managed …

We examine the effect of the introduction of ridehailing in US cities on fatal traffic accidents.
The arrival of ridehailing is associated with an approximately 3% increase in the number of …

Optimizing shared vehicle systems (bike-/scooter-/car-/ride-sharing) are more challenging
compared with traditional resource allocation settings because of the presence of complex …

Motivated by applications from ride-sharing and kidney exchange, we study the problem of
matching agents who arrive at a marketplace over time and leave after d time periods …

We study dynamic pricing of resources that are distributed over a network of locations (eg,
shared vehicle systems and logistics networks). Customers with private willingness-to-pay …

Ride-sharing firms use pricing and matching decisions to control the ride-sharing platforms.
Those decisions can be made jointly or interchangeably, which raises the following …

Mobility systems featuring shared vehicles are often unable to serve all potential customers,
as the distribution of demand does not coincide with the positions of vehicles at any given …

We study the relationship between capacity and performance for a service firm with spatial
operations, in the sense that requests arrive with origin-destination pairs. An example of …