Spatial–Temporal Upfront Pricing Under a Mixed Pooling and Non-Pooling Market With Reinforcement Learning

The on-demand ride-pooling service, defined as two or more passengers sharing the same vehicle en-route along a proportion of their travel trajectories, offers many benefits, such as discounted trip fares for customers, higher income for drivers, increased profit for ride-sourcing companies, and reduced fuel consumption for environmental protection. Motivated by the potential of ride-pooling, many ride-sourcing companies launch pooling services based on the non-pooling market. By providing pooling and non-pooling services simultaneously, they compete with public transit for passengers. Of particular interest to service providers is the upfront pricing problem for the pooling service. It allows pooling riders to be informed of service prices even before the trip starts, and consequently makes the pooling service more attractive to passengers. However, it remains a challenging issue to obtain the optimal spatial-temporal upfront pricing strategy for the pooling service, considering the heterogeneity, dynamics, imbalance of demand /supply, and differentiation between pooling and non-pooling services. To address this problem, two reinforcement learning frameworks (i.e., single-agent Markov Decision Process (MDP) and multi-agent Markov Decision Process (MMDP)) are implemented to gain the pricing policy with the maximum daily profit of the platform, where the pooling price, as the action, not only directly affects the profit of each pooling request, but also has an influence on the mode splitting among pooling, non-pooling, and public transit service. Two tailored reinforcement learning methods are developed and adopted to solve the MDPs. Through extensive empirical experiments with a well-designed simulator, we show that the proposed multi-agent framework is able to remarkably improve the system performance.