A Recommendation Engine for a Smart Parking Ecosystem

Due to waste of time and unnecessary pollution when seeking for a parking space in urban areas, the lack of parking spaces has a serious social and environmental impact. To address this problem, we developed the SocialPARK ecosystem which engages a community of interacting citizens, parking vendors and municipalities. SocialPARK revolves around a crowdsourcing scheme that aggregates parking information, for free public spaces reported by commuters, and for commercial parking spaces offered by parking vendors, in a single integrated platform. SocialPARK offers a variety of services, including "Park-andRide" options, making the city centers more accessible with less pollution. Apart from commuters' participation, a central challenge is the involvement of as many parking vendors as possible, to offer a multitude of parking alternatives to the commuters. We present a business-oriented recommendation engine, developed as part of SocialPARK, to motivate parking vendors to participate in the envisioned open market of parking-related services. The engine is composed of two modules: the first module gathers usage information about the utilization of parking houses within a certain area; (ii) the second module exploits this information to exhibit statistical data and create targeted recommendations for parking houses, rendering their businesses more competitive and improving the services they offer.