  Autonomous mobility on demand systems, though still in their infancy, have
very promising prospects in providing urban population with sustainable and
safe personal mobility in the near future. While much research has been
conducted on both autonomous vehicles and mobility on demand systems, to the
best of our knowledge, this is the first work that shows how to manage
autonomous mobility on demand systems for better passenger experience. We
introduce the Expand and Target algorithm which can be easily integrated with
three different scheduling strategies for dispatching autonomous vehicles. We
implement an agent-based simulation platform and empirically evaluate the
proposed approaches with the New York City taxi data. Experimental results
demonstrate that the algorithm significantly improve passengers' experience by
reducing the average passenger waiting time by up to 29.82% and increasing the
trip success rate by up to 7.65%.
