Taxi-hailing platforms: Inform or Assign drivers?

Abstract

Online platforms for matching supply and demand, as part of the sharing economy, are becoming increasingly important in practice and have seen a steep increase in academic interest. Especially in the taxi/travel industry, platforms such as Uber, Lyft, and Didi Chuxing have become major players. Some of these platforms, including Didi Chuxing, operate two matching systems: Inform, where multiple drivers receive ride details and the first to respond is selected; and Assign, where the platform assigns the driver nearest to the customer. The Inform system allows drivers to select their destinations, but the Assign system minimizes driver-customer distances. This research is the first to explore: (i) how a platform should allocate customer requests to the two systems and set the maximum matching radius (i.e., customer-driver distance), with the objective to minimize the overall average waiting times for customers; and (ii) how taxi drivers select a system, depending on their varying degrees of preference for certain destinations. Using approximate queuing analysis, we derive the optimal decisions for the platform and drivers. These are applied to real-world data from Didi Chuxing, revealing the following managerial insights. The optimal radius is 1-3 kilometers, and is lower during rush hour. For most considered settings, it is optimal to allocate relatively few rides to the Inform system. Most interestingly, if destination selection becomes more important to the average driver, then the platform should not always allocate more requests to the Inform system. Although this may seem counter-intuitive, allocating too many orders to that system would result in many drivers opting for it, leading to very high waiting times in the Assign system.