论文标题
实时和大规模的舰队分配自动出租车：纽约曼哈顿岛的案例研究
Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island
论文作者
论文摘要
如今，自主出租车成为一种非常有前途的运输模式，有助于缓解交通拥堵并避免道路事故。但是，它阻碍了这项服务的广泛实施，即传统模型无法有效地分配可用的车队来应对供应不平衡（自动出租车）和需求（旅行），不良的出租车合作，几乎没有满足的资源限制以及在线平台的要求。为了找出从全球和更远见的看法中找出这种紧急问题，我们采用了受限的多代理马尔可夫决策过程（CMMDP）来建模车队分配决策，可以很容易地将其分为``动态分配问题''，结合了即时奖励和即时奖励和未来的收益。我们还利用列生成算法来确保大规模的效率和最佳性。通过广泛的实验，提出的方法不仅可以从个人的效率方面取得了显着的改善（分别达到12.40％，收入和利用率的增长分别为12.40％，平台上涨6.54％），并且该平台的利润（促销4.59％），还达到了4.59％的促销），但还揭示了一个时空调节的策略，以使平台的运营成本降低了。
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.
