  As efficient traffic-management platforms, public vehicle (PV) systems are
envisioned to be a promising approach to solving traffic congestions and
pollutions for future smart cities. PV systems provide online/dynamic
peer-to-peer ride-sharing services with the goal of serving sufficient number
of customers with minimum number of vehicles and lowest possible cost. A key
component of the PV system is the online ride-sharing scheduling strategy. In
this paper, we propose an efficient path planning strategy that focuses on a
limited potential search area for each vehicle by filtering out the requests
that violate passenger service quality level, so that the global search is
reduced to local search. We analyze the performance of the proposed solution
such as reduction ratio of computational complexity. Simulations based on the
Manhattan taxi data set show that, the computing time is reduced by 22%
compared with the exhaustive search method under the same service quality
performance.
