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Mobile crowdsourcing platform for intelligent car park systems

机译:智能停车场系统的移动众包平台

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Mobile crowdsourcing concept has attracted attentions of researchers, developers and businesses in the last decade. This exact concept has also been used in the area of Intelligent Transportation System (ITS) mainly for collecting and distributing information about the current traffic condition. In this paper, we extend the mobile crowdsourcing concept to be used for an intelligent car park system. Among several existing applications, the system proposed in this paper has been designed so that it is suitable for the car parking behavior exhibited in asian countries. To be specific, the Pick N Park system was proposed and implemented to gather and deliver real-time information about a number of car parks in crowded urban areas. The proposed system not only assists drivers in planning their journeys; but it also helps alleviate the traffic congestion problem especially in front of the shopping malls (where a line of cars waiting to enter the car park blocks other traffic). In addition to the system, we also propose a car park model that can be used to estimate the congestion level and the search time of each car park. This can be useful especially during the initial phase that the technology is introduced. The model has been evaluated by monte-carlo simulations and the results show that the proposed model can provide excellent search time estimates.
机译:在过去的十年中,移动众包概念吸引了研究人员,开发人员和企业的注意力。这种精确的概念也已在智能交通系统(ITS)领域中使用,主要用于收集和分发有关当前交通状况的信息。在本文中,我们扩展了移动众包概念以用于智能停车场系统。在现有的几种应用中,本文提出的系统已被设计为适合于亚洲国家展示的停车行为。具体而言,提出并实施了Pick N Park系统,以收集和传递有关拥挤市区中许多停车场的实时信息。拟议的系统不仅可以帮助驾驶员规划行程,还可以帮助驾驶员规划行程。但这也有助于缓解交通拥堵问题,尤其是在大型购物中心的前面(那里等待进入停车场的一排汽车阻挡了其他交通)。除了该系统外,我们还提出了一个停车场模型,该模型可用于估计每个停车场的拥挤程度和搜索时间。这在引入该技术的初始阶段尤其有用。该模型已通过蒙特卡洛模拟进行了评估,结果表明所提出的模型可以提供出色的搜索时间估计。

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