We propose a novel non-myopic smart parking mechanism which aims to decrease the cruising time spent in searching for parking, with the assumption of elastic demand for both on-street parking lots and parking garages. A non-myopic atomic game is formulated to address competition for parking through assignment of vehicles to candidate parking facilities that takes into account the differences in travel times for the vehicles from their point of origin to the parking facilities and the differences in walking times for the drivers from the parking facilities to their final destination, as well as dynamic pricing, cruising times, and occupancies of the parking facilities. This study integrates a socially efficient price that accounts for the waiting times of drivers in their search for parking. We incorporate a game model into the social optimum problem by considering the competition of drivers for parking spaces where the drivers’ preferences are reflected in a collective decision such as social welfare. Using actual parking data for the city of San Francisco, we found that under our proposed dynamic parking system the average social welfare per vehicle improved by up to 54% compared to other parking strategies.

我们提出了一种新颖的非近视智能停车机制，旨在减少路边停车位和停车库的弹性需求，从而减少寻找停车位所需的巡航时间。制定了一种非近视原子游戏，通过将车辆分配给候选停车设施来解决停车竞争问题，其中考虑了车辆从其原始点到停车设施的行驶时间的差异以及车辆的步行时间的差异。从停车设施到最终目的地的驾驶员，以及动态定价，巡航时间和停车设施的占用率。这项研究综合了一种具有社会效益的价格，该价格考虑了驾驶员在寻找停车位时的等待时间。通过考虑驾驶员对停车位的竞争，将博弈模型纳入社会最优问题，在这种情况下，驾驶员的偏好会反映在集体决策（例如社会福利）中。通过使用旧金山市的实际停车数据，我们发现在我们提出的动态停车系统下，与其他停车策略相比，每辆车的平均社会福利提高了54％。