自动驾驶车辆对包含策略性人类驾驶员的网约车平台的影响
The Impact of Autonomous Vehicles on Ride-Hailing Platforms with Strategic Human Drivers
AI总结：
研究包含策略性人类驾驶员的网约车平台引入自动驾驶车辆后的优化问题，将混合车队系统建模为双层优化问题，提出数值算法求解以最大化平台总利润，并分析了最优车队规模趋势。
摘要：
受自动驾驶车辆技术快速发展的启发，本工作重点关注将其引入包含传统策略性人类驾驶员的网约车平台时所面临的挑战。我们考虑一个运营自动驾驶车辆（AVs）和传统车辆（CVs）混合车队的网约车平台，其中AVs完全由平台控制，而CVs由自利的人类驾驶员运营。每辆车被建模为一个马尔可夫决策过程，通过选择其重新定位动作来最大化长期平均奖励。CVs的行为对应于一个大型博弈，其中智能体通过由排队延迟导致的资源约束进行交互。在我们的流体模型中，当驾驶员供给趋于超过客户服务需求时，驾驶员可能会在不同区域的队列中等待。我们的主要目标是优化混合AV-CV系统，以最大化由AVs和CVs产生的平台总利润。为此，我们将此问题表述为双层优化问题OPT，其中平台首先通过控制AVs的动作和向CVs揭示的需求采取行动，然后CVs对揭示的需求做出反应，形成一个可以通过凸优化问题的解来刻画的均衡。我们证明了最优解的几个有趣结构特性，并分析了简单的启发式方法，例如AV-first方法，即在不考虑CVs后续反应的情况下求解AVs的最优调度。我们提出了三种数值算法来求解OPT，该问题在平台决策参数中是一个非凸问题。我们评估了它们的性能，并利用它们展示了在供给为外生和内生时，最优AV-CV车队规模调整的一些有趣趋势。
英文摘要：
Motivated by the rapid development of autonomous vehicle technology, this work focuses on the challenges of introducing them in ride-hailing platforms with conventional strategic human drivers. We consider a ride-hailing platform that operates a mixed fleet of autonomous vehicles (AVs) and conventional vehicles (CVs), where AVs are fully controlled by the platform and CVs are operated by self-interested human drivers. Each vehicle is modelled as a Markov Decision Process that maximizes long-run average reward by choosing its repositioning actions. The behavior of the CVs corresponds to a large game where agents interact through resource constraints that result in queuing delays. In our fluid model, drivers may wait in queues in the different regions when the supply of drivers tends to exceed the service demand by customers. Our primary objective is to optimize the mixed AV-CV system so that the total profit of the platform generated by AVs and CVs is maximized. To achieve that, we formulate this problem as a bi-level optimization problem OPT where the platform moves first by controlling the actions of the AVs and the demand revealed to CVs, and then the CVs react to the revealed demand by forming an equilibrium that can be characterized by the solution of a convex optimization problem. We prove several interesting structural properties of the optimal solution and analyze simple heuristics such as AV-first where we solve for the optimal dispatch of AVs without taking into account the subsequent reaction of the CVs. We propose three numerical algorithms to solve OPT which is a non-convex problem in the platform decision parameters. We evaluate their performance and use them to show some interesting trends in the optimal AV-CV fleet dimensioning when supply is exogenous and endogenous.
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