TITLE: Matching and Pricing in Ridesharing Systems
Ridesharing platforms are online mobile platforms which match paying customers who need a ride with drivers who provide transportation. Some examples of these platforms are Uber and Lyft in the USA, Didi Chuxing in China, Ola in India, and Grab in Southeast Asia. When a customer requests a ride, the ridesharing firm should charge a price and offer a driver to the customer. The matching 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. The pricing decisions are important because they have opposite effect on the customer demand and driver supply. As the price in an area increases, customer demand decreases but the driver supply (roughly speaking) increases in that area.
We present a ridesharing model in which the customer and driver arrival rates depend on the pricing and matching decisions of the firm. This is a very complicated problem, and so there is a temptation to fix a simple matching policy that matches an arriving customer with the closest available driver, and to then focus on optimizing the price. However, we show that when customer valuations for the price are heterogeneous, the matching decisions have a significant impact on system performance. This motivates us to more closely study the matching decisions. To do this, we assume the pricing has been fixed and then propose a matching policy that is based on the solution to a continuous linear program (CLP). That CLP 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 further show that when drivers are fully utilized, the matching decisions can be myopically based on the solution to a linear program without sacrificing performance.
*Based on a joint paper with Amy R. Ward and a joint paper with Ramandeep Randhawa and Amy R. Ward.
BIO: Erhun Özkan is a Ph.D. candidate in the Data Sciences and Operations department of the Marshall School of Business, University of Southern California. His research interests include the applications of probability theory, queueing theory, and stochastic processes in matching markets, revenue management, and healthcare systems. During his Ph.D. studies, he worked on matching and pricing decisions in ridesharing systems and scheduling decisions in networks with both parallel and sequential processing constraints, aka fork-join networks. Erhun Özkan received his BS degree in industrial engineering from Middle East Technical University, Turkey.