Taxis are an important transportation mode in many cities due to their convenience and accessibility. In the taxi-dispatching problem, sometimes it is more beneficial for the supplier if taxis cruise in the network after serving the first request to pick up the next passenger, while sometimes it is better that they wait in stations for new trip requests. In this article, we propose a rolling-horizon scheme that dynamically optimizes taxi dispatching considering the actual traffic conditions. To optimize passenger satisfaction, we define a limitation for passenger waiting time. To be able to apply the method to large-scale networks, we introduce a clustering-based technique that can significantly improve the computation time without harming the solution quality. Finally, we test our method on a real test case considering taxi requests with personal car trips to reproduce actual network loading and unloading congestion during peak hours.

中文翻译：

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一种考虑交通动态的出租车调度问题的序列聚类方法

出租车因其便捷性和可达性成为许多城市的重要交通方式。在出租车调度问题中，有时出租车在服务了第一个乘客的请求后在网络中巡航对供应商更有利，而有时他们最好在车站等待新的出行请求。在本文中，我们提出了一种滚动水平方案，可根据实际交通状况动态优化出租车调度。为了优化乘客满意度，我们定义了乘客等待时间的限制。为了能够将该方法应用于大规模网络，我们引入了一种基于聚类的技术，该技术可以在不损害解决方案质量的情况下显着提高计算时间。最后，