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Incentive Design in Ride Sharing Platforms
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    Final Research Report
  • Abstract:
    Ride sharing platforms have quickly become a widespread phenomenon. However, mismatches between supply and demand at peak times (temporal mismatch) or in some locations (spatial mismatch) motivate the design of incentive systems to align the behaviors of riders and drivers. This project proposes a novel economic mechanism that leverages new technological capabilities to improve the performance of ride-sharing systems and urban mobility. The proposed mechanism enhances existing practices and the prior academic literature by providing discriminated prices and service levels across heterogeneous customers and by adjusting the menu of prices and service levels over time depending on realized endogenous and exogenous contingencies. 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 outcomes of this project include a 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; and new insights on the performance of on-demand platforms, and opportunities for personalized pricing and service levels.
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