Future-Aware Balanced Preference Matching for Real-Time On-Demand Taxi Dispatch

Abstract

Spatial crowdsourcing is drawing much attention with the rapid development of mobile Internet. Achieving efficient crowdsourcing task assignment involves not only maximizing the earnings of workers but also balancing the preferences of users or customers. Users often express preferences for specific workers or conditions, such as particular drivers, delivery personnel, or service providers. To address this challenge, we investigate the future-aware balanced preference (FABP) problem. This problem aims to maximize the profits of global workers while simultaneously considering the preferences of both parties to ensure bilateral satisfaction. To address the FABP problem, we propose the learning to match (LTM) algorithm. This algorithm utilizes online reinforcement learning that considers both immediate profits and long-term rewards. It acknowledges the significance of task assignment decisions in relation to the spatial distribution of future drivers, which in turn affects subsequent decisions. The LTM algorithm generates future-aware preference lists using learned driver state values and guides the subsequent matching. Additionally, we present the real-time preference-based matching (RTPM) algorithm, which is a real-time matching algorithm that enables substitutions based on preference lists when a more preferred matching pair becomes available. This enhances the efficiency and fairness of real-time task assignment in dynamic environments, while simultaneously meeting the needs of passengers and drivers. Our extensive experiments on both real and synthetic datasets validate the effectiveness of our proposed algorithms, demonstrating a noteworthy improvement of up to 11.8% and an average increase of 4.7% compared to benchmark algorithms.