A Deep Reinforcement Learning Based Dynamic Pricing Algorithm in Ride-Hailing

Abstract

Online ride-hailing has become one of the most important transportation ways in the modern city. In the ride-hailing system, the vehicle supply and riding demand is different in different regions, and thus the passengers’ willingness to take a riding service will change dynamically. Traditional pricing strategies cannot make reasonable decisions to set the riding prices with respect to the dynamical supply and demand in different regions, and they cannot make adaptive responses to the real-time unbalanced supply and demand. In addition, the ride-hailing platform usually intends to maximize the long-term profit. In this paper, we use deep reinforcement learning to design a multi-region dynamic pricing algorithm to set the differentiate unit price for different regions in order to maximize the long-term profit of the platform. Specifically, we divide the ride-hailing area into several non-overlapping regions, and then propose a model to characterize the passenger’s price acceptance probability. We further model the pricing issue as a Markov decision-making process, and then use deep reinforcement learning to design a multi-region dynamic pricing algorithm (MRDP) to maximize the platform’s long-term profit. We further run extensive experiments based on realistic data to evaluate the effectiveness of the proposed algorithm against some typical benchmark approaches. The experimental results show that MRDP can set the price effectively based on supply and demand to make more profit and can balance the supply and demand to some extent.