Paccar Hall, Room 290

Seminar Speaker: Amy Ward
Affiliation: USC
Area: Operations Management

Name of Presentation: Dynamic Matching for Real-Time Ridesharing

Abstract:
In a ridesharing system such as Uber or Lyft, arriving customers must be matched with available drivers.  These decisions affect the overall number of customers matched, because they impact whether or not future available drivers will be close to the locations of arriving customers. A common policy used in practice is the closest driver (CD) policy that offers an arriving customer the closest driver. This is an attractive policy because no parameter information is required. However, we expect that a parameter-based policy can achieve better performance.
We propose to base the matching decisions on the solution to a continuous linear program (CLP) that accounts for (i) the differing arrival rates of customers and drivers in different areas of the city, (ii) how long customers are willing to wait for driver pick-up, and (iii) the time-varying nature of all the aforementioned parameters. We prove asymptotic optimality of a CLP-based policy in a large market regime.  However, solving the CLP is difficult, thus we also propose matching policies based on a linear program (LP).  We prove asymptotic optimality of an LP-based policy in a large market regime in which drivers are fully utilized.  We conduct simulation experiments to test the performance of the CD, LP-based, and CLP-based policies.