Two-Sided Instant Incentive Optimization under a Shared Budget in Ride-Hailing Services

Ride-hailing has become a popular service in recent years. For each ride-hailing request, after the platform determines the fare for the passenger and the commission for the driver, it is not uncommon for the platform to set aside a promotional budget and give instant incentives to both sides, that is, a discount to the passenger and a bonus to the driver, to further improve the match between the two sides. Although there is a proliferation of studies on the determination of the fare and the commission in ride-hailing services, they cannot address the instant incentive problem because their approaches do not deal with budget constraints. In this research, we investigate this two-sided instant incentive problem under a shared promotional budget, which is new to the literature. We formulate this problem as a binary integer linear programming problem, whose goal is to find the optimal incentives for both sides given predicted trip completion probabilities. We first assume that the predicted trip completion probabilities are accurate and develop a Lagrangian-dual-based approach to decompose the problem into a series of subproblems that can be efficiently solved. We then proceed to accommodate the inaccuracy in the predictions and develop a robust instant incentive optimization approach that exploits the prediction error reflected by historical data. We conduct numerical experiments using real data in the city of Nanjing from a leading ride-hailing platform in China. Results show that compared to the baseline approach: (i) Before we account for prediction inaccuracy, our solution approach can improve the number of completed requests by at most 8.30% with a decision error of 8.31%; and (ii) After we account for prediction inaccuracy, our solution approach can improve the number of completed requests by at most 9.81% while reducing the decision error to 7.03%.