Dynamic pricing and fleet management for electric autonomous mobility on demand systems
Dynamic pricing and fleet management for electric autonomous mobility on demand systems
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DOI：
10.1016/j.trc.2020.102829
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Berkay Turan;Ramtin Pedarsani;M. Alizadeh
中科院分区：
文献类型：
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作者：
Berkay Turan;Ramtin Pedarsani;M. Alizadeh
拼车系统的普及是自动驾驶汽车和电动汽车技术进步的主要推动力。本文考虑了一个利润最大化的运输服务提供商所面临的联合路线、电池充电和定价问题，该运输服务提供商运营着一个自动驾驶电动汽车车队。首先考虑定常系统参数，建立静态规划问题，确定最优静态策略。虽然静态策略提供了客户排队等待乘车的稳定性，但即使考虑到系统动态，我们也看到使用静态策略是低效的，因为它可能导致客户等待时间长，利润低。为了适应出行需求的随机性、可再生能源的可用性和电价，并在需要生成整数分配的情况下进一步优化管理自动车队，需要一个实时策略。基于系统的全状态信息执行动作的最优实时策略是一个复杂动态规划的解决方案。然而，我们认为使用精确动态规划方法精确求解最优策略是难以解决的，因此应用深度强化学习来开发近最优控制策略。我们在曼哈顿和旧金山进行的两个案例研究证明了实时策略在网络稳定性和利润方面的有效性，同时使队列长度比静态策略少200倍。
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.