Project: #187 Incentive Design in Ride Sharing Platforms Progress Report - Reporting Period Ending: Sept. 30, 2018 Principal Investigator: Alexandre Jacquillat Status: Active Start Date: Sept. 1, 2018 End Date: Aug. 31, 2020 Research Type: Applied Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2018 Mobility21 UTC Progress Report (Last Updated: Sept. 29, 2018, 7:13 a.m.) % Project Completed to Date: 75 % Grant Award Expended: 30 % Match Expended & Document: 30 USDOT Requirements Accomplishments Despite their short history, ride sharing platforms have quickly become a widespread phenomenon, and have profound effects on urban transportation systems. At the same time, the ultimate benefits of these platforms might be hindered by mismatches between supply and demand at peak times (temporal mismatch) or in some locations (spatial mismatch). This motivates the design of incentive systems to align the behaviors of riders and drivers, to make the economic and operational outcomes more consistent with agents’ preferences. On-demand ride-sharing differs from traditional transportation systems in three aspects. First, ride-sharing manages demand-supply imbalances in real-time (as opposed to transit scheduling). Second, ride-sharing relies on the online matching of riders and sellers (as opposed to first-come first-served operations in taxi systems). Third, online payment capabilities enable real-time personalized pricing schemes (e.g., surge pricing applies differentiated prices based on spatial- temporal characteristics of rider requests and driver supply). This project proposes a novel economic mechanism that leverages these new capabilities to improve the performance of ride-sharing systems and urban mobility. This mechanism augments existing surge pricing practices when riders are heterogeneous. Indeed, surge pricing relies exclusively on public information, and may thus result in lost revenue opportunities and mismatches between service offers and customers’ expectations. Instead, the proposed mechanism elicits customer preferences to provide personalized pricing and service levels. From a practical standpoint, the mechanism works as follows. Rider A and Rider B request a ride at the same time from the same origin to the same destination. Rider A is in a hurry, and thus willing to pay a premium for getting a ride right away. Rider B does not mind waiting, but would love a discount if possible. In the current state of operations, ride-sharing platforms cannot differentiate prices and services among the two riders. So Rider B may get a ride before Rider A! The proposed mechanism, in contrast, enables Riders A and B to let the platform know about their preferences, which enables the platform to account for them when allocating rides. The design and optimization of the proposed mechanism is achieved through a theoretical economics model that optimizes the platform’s profits, subject to individual rationality, incentive compatibility and capacity constraints. The resulting insights fall into four categories: • Ride-sharing platforms can use the timing of rides strategically for: (i) smoothing out demand-supply imbalances, and (ii) discriminating service levels across time-sensitive and price-sensitive riders. • There exist instances where the optimal price of a ride is higher under low demand than under high demand, due to the dual objectives of managing demand-supply imbalances and discriminating across heterogeneous riders. • Under strong rider heterogeneity, the mechanism does not maximize social welfare but the platform captures all the surplus generated. Under weak rider heterogeneity, the mechanism maximizes social welfare but the platform leaves some surplus to the riders as information rent. As compared to surge pricing, the mechanism increases the platform’s profit. Moreover, by making the allocation decisions more consistent with customer preferences, it may also induce a higher customer surplus, thus providing a Pareto improvement. Impacts The outcomes of this project include: • New theoretical economics model for on-demand ride-sharing and analytical results characterizing optimal ride-sharing operations, as a function of rider heterogeneity and demand-supply imbalances faced by the platform. • New insights on the performance of on-demand platforms, and opportunities for personalized pricing and service levels. Results suggest opportunities to enhance the economic and operational performance of ride-sharing platforms by eliciting customer preferences and adjusting prices and service levels accordingly. The insights from this project are in line with recent industrial developments such as Uber Pool, Uber Express Pool and Lyft Line. These provide differentiated services that implicitly account for heterogeneity in time preferences. In this project, we design a mechanism that explicitly achieves similar objectives without resorting to the development of new products or services. In fact, Kakao Taxi, the dominant ride-sharing company in Korea, recently launched a new option to enable fast pickup at a price premium. This provides a prime example of the type of time discriminatory mechanism proposed here. Ultimately, this project can inform the design of pricing and service levels for ride-sharing platforms to make them more consistent with riders’ and drivers’ preferences while increasing profits, thus increasing the overall performance of on-demand urban transportation systems. Other . New Partners . Issues .