Dynamic Pricing and Fleet Management for Electric Autonomous Mobility on Demand Systems
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 autonomou...
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Zusammenfassung: | 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 first establish the static planning problem by considering
time-invariant system parameters and determine the optimal static policy. While
the static policy provides stability of customer queues waiting for rides even
if consider the system dynamics, we see that it is inefficient to utilize a
static policy as it can lead to long wait times for customers and low profits.
To accommodate for the stochastic nature of trip demands, renewable energy
availability, and electricity prices and to further optimally manage the
autonomous fleet given the need to generate integer allocations, a real-time
policy is required. The optimal real-time policy that executes actions based on
full state information of the system is the solution of a complex dynamic
program. However, 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. The two case
studies we conducted in Manhattan and San Francisco demonstrate the efficacy of
our real-time policy in terms of network stability and profits, while keeping
the queue lengths up to 200 times less than the static policy. |
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DOI: | 10.48550/arxiv.1909.06962 |