This paper proposes an (automated) on-demand public transport service using different vehicle capacities to serve current car demand in cities. The service relies on space and time aggregation of passengers that have similar origins and destinations. It provides a point-to-point service with predefined pick-up and drop-off locations. In this way, detours to pick-up en-route passengers is avoided. The optimization problem that minimizes the fleet size along with limiting rebalancing distances is defined as a mixed-integer linear programming problem. Solving the problem for Zurich, Switzerland, yields, in the best case, a fleet size equal to 3.7% of the current fleet that could serve current car demand. Vehicle kilometers traveled could also be reduced by nearly 10%. Results also show that the speed of automated vehicles has a substantial effect on the necessary fleet size and free-flow speeds generally produce over-optimistic results.

本文提出了一种（自动）按需公共交通服务，该服务使用不同的车辆容量来满足城市当前的汽车需求。该服务依赖于具有相似出发地和目的地的乘客的时空聚集。它提供了具有预定义的接送地点的点对点服务。这样，避免了绕行途中接载乘客的路线。最小化车队规模以及限制重新平衡距离的优化问题被定义为混合整数线性规划问题。解决瑞士苏黎世的问题，在最好的情况下，其车队规模相当于当前车队的3.7％，可以满足当前的汽车需求。行驶的车辆公里数也可以减少近10％。