  Technology of autonomous vehicles (AVs) is getting mature and many AVs will
appear on the roads in the near future. AVs become connected with the support
of various vehicular communication technologies and they possess high degree of
control to respond to instantaneous situations cooperatively with high
efficiency and flexibility. In this paper, we propose a new public
transportation system based on AVs. It manages a fleet of AVs to accommodate
transportation requests, offering point-to-point services with ride sharing. We
focus on the two major problems of the system: scheduling and admission
control. The former is to configure the most economical schedules and routes
for the AVs to satisfy the admissible requests while the latter is to determine
the set of admissible requests among all requests to produce maximum profit.
The scheduling problem is formulated as a mixed-integer linear program and the
admission control problem is cast as a bilevel optimization, which embeds the
scheduling problem as the major constraint. By utilizing the analytical
properties of the problem, we develop an effective genetic-algorithm-based
method to tackle the admission control problem. We validate the performance of
the algorithm with real-world transportation service data.
