This paper studies policy evaluation with multiple data sources, especially in scenarios that involve one experimental dataset with two arms, complemented by a historical dataset generated under a single control arm. We propose novel data integration methods that linearly integrate base policy value estimators constructed based on the experimental and historical data, with weights optimized to minimize the mean square error (MSE) of the resulting combined estimator. We further apply the pessimistic principle to obtain more robust estimators, and extend these developments to sequential decision making. Theoretically, we establish non-asymptotic error bounds for the MSEs of our proposed estimators, and derive their oracle, efficiency and robustness properties across a broad spectrum of reward shift scenarios. Numerical experiments and real-data-based analyses from a ridesharing company demonstrate the superior performance of the proposed estimators.

The online ride-hailing platform has revolutionized urban transport. However, there is much room for improvement. We consider meeting the demand for an online ride-hailing at transport hubs late at night, when the public transport system stops its operations. Passengers arriving late at night face a long wait before service. We launch the ride-hailing model in the theoretic framework of queueing and introduce the arrival and the service processes. To improve the efficiency of the ride-hailing platform, as well as to maintain fairness between different types of passengers, we study three variations of carpool service policies. We then provide practical guidelines on the trade-off between efficiency and fairness to assist the online platform designers. Specifically, we derive the analytical trade-off bounds with the passenger parameters. Furthermore, we suggest that these bounds can be good performance estimators for the empirical trade-off when only limited passenger information is available. This analysis motivates us to design the optimal service rate for the entire platform. Finally, we conduct numerical studies based on field data retrieved from Didi Chuxing, highlighting the remarkable performance of our proposed method in terms of improving the quality of online ride-hailing service.