Can dynamic ride-sharing reduce traffic congestion? In this paper we show that the answer is yes if the trip density is high, which is usually the case in large-scale networks but not in medium-scale networks where opportunities for sharing in time and space become rather limited. When the demand density is high, the dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Sharing can compensate extra travel distances related to operating a mobility service. The situation is entirely different in small and medium-scale cities when trip shareability is small, even if the ride-sharing system is fully optimized based on the perfect demand prediction in the near future. The reason is simple, mobility services significantly increase the total travel distance, and sharing is simply a means of combating this trend without eliminating it when the trip density is not high enough. This paper proposes a complete framework to represent the functioning of the ride-sharing system and multiple steps to tackle the curse of dimensionality when solving the problem. We address the problem for two city scales in order to compare different trip densities. A city scale of 25 $k{m}^2$ with a total market of 11,235 shareable trips for the medium-scale network and a city scale of 80 $k{m}^2$ with 205,308 demand for service vehicles for the large-scale network over a 4-hour period with a rolling horizon of 20 minutes. The solutions are assessed using a dynamic trip-based macroscopic simulation to account for the congestion effect and dynamic travel times that may influence the optimal solution obtained with predicted travel times. This outperforms most previous studies on optimal fleet management that usually consider constant and fully deterministic travel time functions.

动态乘车共享可以减少交通拥堵吗？在本文中，我们表明，如果行程密度很高，答案是肯定的，这在大型网络中通常是这样，而在时间和空间共享机会变得非常有限的中型网络中则不是。当需求密度很高时，动态乘车共享系统可以显着改善交通状况，尤其是在高峰时段。共享可以补偿与运营出行服务相关的额外旅行距离。即使在不久的将来基于理想需求预测对乘车共享系统进行了充分优化，中小城市的出行共享率也完全不同。原因很简单，出行服务大大增加了总旅行距离，当旅行密度不够高时，共享只是消除这种趋势的一种方法，而不会消除它。本文提出了一个完整的框架来表示乘车共享系统的功能，并提出了解决问题时维数诅咒的多个步骤。我们比较两个城市规模的问题，以比较不同的出行密度。25城市规模$ķ{米}^2$ 中型网络的总行车市场为11,235人，城市规模为80 $ķ{米}^2$在长达20分钟的4小时内，有205,308辆大型网络的服务车辆需求。使用基于动态行程的宏观模拟评估解决方案，以解决可能影响通过预测行程时间获得的最佳解决方案的拥塞效应和动态行程时间。这优于以往有关最佳车队管理的大多数研究，后者通常考虑恒定且完全确定的旅行时间功能。