On-Trip Matching and Pricing for Shared Rides
Although shared rides have the potential to increase vehicle utilization and reduce congestion and emissions, these benefits depend heavily on ridesharing platforms' ability to match riders effectively. As such, shared rides have seen limited success outside of dense urban areas -- the sparse outskirts of greater metropolitan areas remain underserved. In the literature, the dominant matching model involves collecting rider requests in a batch interval and solving a non-bipartite matching problem on the requests. However, this model neglects the ability of a rider to be matched to a future arriving rider even after she is initially dispatched solo; namely, matching is only modeled pre-trip, and the value of on-trip matching is not explicitly accounted for. We develop a dynamic, stochastic matching model, where the platform makes both pre-trip and on-trip matching decisions, and contrast the behavior of each phase of matching. Using both synthetic and real-world data from Chicago, we find that whereas pre-trip matching is well-suited to dense downtown areas with concentrated demand, on-trip matching is critical in sparser outskirts where demand is spatially dispersed, and manages a tradeoff between matching opportunity and value. We also embed the matching model in an outer pricing optimization problem to study the interaction of matching with pricing, and find that the addition of on-trip matching increases profitability and efficiency for the platform and lowers prices for riders. These effects are particularly pronounced in the sparse outskirts, where operating shared rides -- even providing access to any form of transportation -- has historically been most challenging.