The proliferation of mobile devices, location-based services and embedded wireless sensors has given rise to applications that seek to improve the efficiency of the transportation system. In particular, new applications are arising that help travelers find parking in urban settings. They convey the parking slot availability around users on their mobile devices. Nevertheless, while engaged in driving, travelers are better suited being guided to an ideal parking slot, than looking at a map and deciding which open slot to visit. Then the question of how an application should choose this ideal parking slot to guide the user towards it becomes relevant. Vehicular parking can be viewed as vehicles (players) competing for parking slots (resources with different costs). Based on this competition, we present a game-theoretic framework to analyze parking situations. We introduce and analyze parking slot assignment games and present algorithms that choose parking slots ideally in competitive parking simulations. We also present algorithms for incomplete information contexts and show how these algorithms outperform greedy algorithms in most situations.