Opportunistically assisted parking service discovery: Now it helps, now it does not

In this paper we explore the way the discovery of service can be facilitated or not by utilizing service location information that is opportunistically disseminated primarily by the service consumers themselves. We apply our study to the realworld case of parking service in busy city areas. As the vehicles drive around the area, they opportunistically collect and share with each other information on the location and status of each parking spot they encounter. This opportunistically assisted scenario is compared against one that implements a“blind”non-assisted search and a centralized approach, where the allocation of parking spots is managed by a central server with global knowledge about the parking space availability. Results obtained for both uniformly distributed travel destinations and a single hotspot destination reveal that the relative performance of the three solutions can vary significantly and not always inline with intuition. Under the hotspot scenario, the opportunistic system is consistently outperformed by the centralized system, which yields the minimum times and distances at the expense of more distant parking spot assignments; whereas, for uniformly distributed destinations, the relative performance of all three schemes changes with the vehicle volume, with the centralized approach gradually becoming the worst solution and the opportunistic one emerging as the best scheme. We discuss how each approach modulates the information dissemination process in space and time and resolves the competition for the parking resources. We also outline models providing analytical insights to the behavior of the centralized approach.