Matching and Pricing in Ridesharing Systems
Data Sciences and Operations, Marshall School of Business, University of Southern California
Dec 22, Friday 13:40
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.
Since customer demand and driver supply change dramatically over time, an ideal ridesharing model should have time dependent parameters and the customer and driver arrival rates should depend on the pricing and matching decisions. However, such a model is very difficult to analyze. Therefore, we first present a model in which the prices are given, customer and driver arrival rates are time dependent but exogenous, and we optimize the matching decisions. 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 forward-looking CLP-based policy in a large market regime. We leverage that result to also prove the asymptotic optimality of a myopic LP-based matching policy when drivers are fully utilized.
Second, we present a model in which the customer and driver behaviors are endogenous but the parameters are time homogeneous. We jointly optimize the pricing and matching decisions. We derive conditions under which simple pricing and matching decisions are optimal. Moreover, we quantify the benefits of complicated pricing and matching decisions.
*Based on a joint paper with Amy R. Ward and a joint paper with Ramandeep Randhawa and Amy R. Ward.
Brief bio of the speaker
Erhun Özkan is a Ph.D. candidate in the Data Sciences and Operations department of the Marshall School of Business, University of Southern California. During his Ph.D. studies, he worked on operational problems in ridesharing systems and fork-join processing networks. Erhun Özkan received his BS degree in industrial engineering from METU. He received a joint master degree in industrial engineering from METU and Eindhoven University of Technology, and a master degree in industrial engineering from University of Pittsburgh.