Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning
This addresses the need for ride-hailing platforms to optimize operational strategies in a competitive market, though it is incremental as it combines existing methods for a specific application.
The paper tackles the problem of predicting driver behaviors in ride-hailing services by developing a simulation framework using imitation and reinforcement learning, enabling platforms to experiment with parameters like fares and incentives without real-world risks.
The rapid growth of ride-hailing platforms has created a highly competitive market where businesses struggle to make profits, demanding the need for better operational strategies. However, real-world experiments are risky and expensive for these platforms as they deal with millions of users daily. Thus, a need arises for a simulated environment where they can predict users' reactions to changes in the platform-specific parameters such as trip fares and incentives. Building such a simulation is challenging, as these platforms exist within dynamic environments where thousands of users regularly interact with one another. This paper presents a framework to mimic and predict user, specifically driver, behaviors in ride-hailing services. We use a data-driven hybrid reinforcement learning and imitation learning approach for this. First, the agent utilizes behavioral cloning to mimic driver behavior using a real-world data set. Next, reinforcement learning is applied on top of the pre-trained agents in a simulated environment, to allow them to adapt to changes in the platform. Our framework provides an ideal playground for ride-hailing platforms to experiment with platform-specific parameters to predict drivers' behavioral patterns.