基于协作系统的智能车位分配

Sofiene Abidi, A. Çela, R. Natowicz
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引用次数: 1

摘要

“智能停车”的先进研究解决方案涉及使用车辆合作,通过车辆特设网络共享停车信息。在寻找停车位时,解决方案可能是机会主义的,这可能会增加竞争水平,从而增加搜索时间。考虑到停车资源的限制,我们需要通过放宽合作驾驶员的约束,从而扩大搜索空间的可行性,将车辆合作提升到一个新的水平。提出了一种基于协作系统的智能停车模型;一个奖励制度,通过放松合作司机的限制,使资源得到更好的利用,特别是在高峰时段,从而有助于提高停车分配效率。奖励系统中的订阅司机将能够管理由交通引起的延迟,因为系统将根据司机到达目的地的剩余旅行时间动态调整停车位分配。为了实现大规模停车优化,使用SUMO/MATLAB框架模拟了一个真实的移动场景。仿真结果表明,与非合作停车相比,该模型降低了平均停车失败率和出行成本,整体停车容量得到了更有效的利用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Parking Space Allocation Based on a Cooperative System
Advanced research solutions for ‘smart parking’ involve the use of vehicular cooperation to share parking information via vehicular ad hoc network. Solutions can be opportunistic when looking for parking spaces, which can increase the competing level and in turn, increase the searching time. Considering the parking resource limitations, we need to take vehicular cooperation to a next level by relaxing the constraints of cooperating drivers and so expanding the feasibility of the search space. This paper presents smart parking model Based on a Cooperative System; a Reward System that allows better use of resources especially during peak hours, by relaxing the constraints of cooperating drivers and so contributing to parking assignment efficiency increase. Subscribed drivers in the Reward System will be able to manage their delays induced by the traffic, since a readjustment of the parking space allocation it will be made dynamically by the system, based on the driver remaining travel time to his destination. In order to work with large-scale parking optimization, a real case mobility scenario is simulated using a SUMO/MATLAB framework. Based on simulation results, compared with non-cooperative parking processes, our model reduces the average parking rate failure and the traveling cost, whereas the overall parking capacity is more efficiently utilized.
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