Dynamic Pricing and Management for Electric Autonomous Mobility on Demand Systems Using Reinforcement Learning

The proliferation of ride sharing systems is a major drive in the advancement of autonomous and electric vehicle technologies. This paper considers the joint routing, battery charging, and pricing problem faced by a profit-maximizing transportation service provider that operates a fleet of autonomous electric vehicles. We define the dynamic system model that captures the time dependent and stochastic features of an electric autonomous-mobility-on-demand system. To accommodate for the time-varying nature of trip demands, renewable energy availability, and electricity prices and to further optimally manage the autonomous fleet, a dynamic policy is required. In order to develop a dynamic control policy, we first formulate the dynamic progression of the system as a Markov decision process. We argue that it is intractable to exactly solve for the optimal policy using exact dynamic programming methods and therefore apply deep reinforcement learning to develop a near-optimal control policy. Furthermore, we establish the static planning problem by considering time-invariant system parameters. We define the capacity region and determine the optimal static policy to serve as a baseline for comparison with our dynamic policy. While the static policy provides important insights on optimal pricing and fleet management, we show that in a real dynamic setting, it is inefficient to utilize a static policy. The two case studies we conducted in Manhattan and San Francisco demonstrate the efficacy of our dynamic policy in terms of network stability and profits, while keeping the queue lengths up to 200 times less than the static policy.