Nowadays, autonomous taxis become a highly promising transportation mode, which helps relieve traffic congestion and avoid road accidents. However, it hinders the wide implementation of this service that traditional models fail to efficiently allocate the available fleet to deal with the imbalance of supply (autonomous taxis) and demand (trips), the poor cooperation of taxis, hardly satisfied resource constraints, and on-line platform's requirements. To figure out such urgent problems from a global and more farsighted view, we employ a Constrained Multi-agent Markov Decision Processes (CMMDP) to model fleet allocation decisions, which can be easily split into sub-problems formulated as a 'Dynamic assignment problem' combining both immediate rewards and future gains. We also leverage a Column Generation algorithm to guarantee the efficiency and optimality in a large scale. Through extensive experiments, the proposed approach not only achieves remarkable improvements over the state-of-the-art benchmarks in terms of the individual's efficiency (arriving at 12.40%, 6.54% rise of income and utilization, respectively) and the platform's profit (reaching 4.59% promotion) but also reveals a time-varying fleet adjustment policy to minimize the operation cost of the platform.

中文翻译：

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自动驾驶出租车的实时大规模车队分配：以纽约曼哈顿岛为例

如今，自动驾驶出租车成为一种极具发展前景的交通方式，有助于缓解交通拥堵，避免道路交通事故。然而，传统模式无法有效分配可用车队以应对供需（自主出租车）和需求（出行）不平衡、出租车合作不畅、资源约束难以满足等问题，阻碍了这项服务的广泛实施。 -line 平台的要求。为了从全局和更有远见的角度找出这些紧迫的问题，我们采用受约束的多智能体马尔可夫决策过程 (CMMDP) 来对车队分配决策进行建模，该决策可以轻松拆分为制定为“动态分配问题”的子问题结合眼前的回报和未来的收益。我们还利用列生成算法来保证大规模的效率和最优性。通过大量实验，所提出的方法不仅在个人效率（收入和利用率分别增长 12.40%、6.54%）和平台利润（达到4.59% 促销）同时也揭示了时变机队调整政策，以最大限度地降低平台的运营成本。