Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Istituto di Informatica e Telematica del CNR, Pisa, Italy.
Nature. 2018 May;557(7706):534-538. doi: 10.1038/s41586-018-0095-1. Epub 2018 May 23.
Information and communication technologies have opened the way to new solutions for urban mobility that provide better ways to match individuals with on-demand vehicles. However, a fundamental unsolved problem is how best to size and operate a fleet of vehicles, given a certain demand for personal mobility. Previous studies either do not provide a scalable solution or require changes in human attitudes towards mobility. Here we provide a network-based solution to the following 'minimum fleet problem', given a collection of trips (specified by origin, destination and start time), of how to determine the minimum number of vehicles needed to serve all the trips without incurring any delay to the passengers. By introducing the notion of a 'vehicle-sharing network', we present an optimal computationally efficient solution to the problem, as well as a nearly optimal solution amenable to real-time implementation. We test both solutions on a dataset of 150 million taxi trips taken in the city of New York over one year . The real-time implementation of the method with near-optimal service levels allows a 30 per cent reduction in fleet size compared to current taxi operation. Although constraints on driver availability and the existence of abnormal trip demands may lead to a relatively larger optimal value for the fleet size than that predicted here, the fleet size remains robust for a wide range of variations in historical trip demand. These predicted reductions in fleet size follow directly from a reorganization of taxi dispatching that could be implemented with a simple urban app; they do not assume ride sharing, nor require changes to regulations, business models, or human attitudes towards mobility to become effective. Our results could become even more relevant in the years ahead as fleets of networked, self-driving cars become commonplace.
信息和通信技术为城市交通提供了新的解决方案，使人们能够更好地匹配按需车辆。然而，一个尚未解决的基本问题是，在个人出行需求一定的情况下，如何最好地规划和运营车辆车队。以前的研究要么没有提供可扩展的解决方案，要么需要改变人们对出行的态度。在这里，我们为以下“最小车队问题”提供了一种基于网络的解决方案，即给定一系列行程（指定出发地、目的地和出发时间），如何确定服务所有行程而不使乘客产生任何延误所需的最小车辆数量。通过引入“车辆共享网络”的概念，我们提出了一种最优的、计算效率高的解决方案，以及一种适用于实时实现的近最优解决方案。我们在纽约市一年的 1.5 亿次出租车行程数据集上测试了这两种解决方案。该方法的实时实现具有接近最优的服务水平，与当前的出租车运营相比，车队规模减少了 30%。尽管驾驶员可用性的限制和异常行程需求的存在可能导致车队规模的最优值相对较大，但车队规模对于历史行程需求的广泛变化仍然具有稳健性。这些车队规模的预测减少直接源于出租车调度的重新组织，这可以通过一个简单的城市应用程序来实现；它们不假设拼车，也不需要改变法规、商业模式或人们对出行的态度，就可以生效。随着联网自动驾驶汽车车队变得越来越普遍，我们的研究结果在未来几年可能会变得更加相关。
