智慧城市背景信息让停车更便捷:实现停车资源的快速搜索和管理

J. Rico, Juan Sancho, B. Cendón, M. Camus
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引用次数: 59

摘要

在绝大多数城市,由于停车位几乎满员,增加停车位既困难又不昂贵。这一问题与停车位的低效使用相结合,导致停车寻求者和普通司机聚集在一起,导致交通拥堵。低成本、低功耗嵌入式系统的最新进展为开发新的应用程序来解决这些问题带来了机会。特别是,智慧城市通过引入新的资源管理应用程序,极大地丰富了其可持续性,这些应用程序依赖于那些受限制的设备,这是系统功能的重要组成部分。提出的智能停车解决方案主要包括现场部署物联网解决方案,以监控和通知每个单个停车位的可用状态,并利用城市和市民产生的上下文信息,为驾驶员的需求提供准确的响应。此外,该系统提高了公共部门对停车资源的管理,例如处理停车位组,方便整个城市的交通管理。将此部署集成到现有的实时测试平台意味着只需通过可用的停车位收集数据就可以轻松完成任务。目前,存在可用于集成这些新功能以进行物联网数据级实验的活体试验台,以更好地了解和理解M2M世界,降低成本,资源,污染和时间。
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Parking Easier by Using Context Information of a Smart City: Enabling Fast Search and Management of Parking Resources
In the great majority of cities it is difficult and hardly expensive to create more parking spaces for vehicles since they have almost reached its full occupancy. Combining this problem with an inefficient use of parking spaces leads to congestions due to aggregation of parking seekers and regular drivers. Recent advances in low-cost, low-power embedded systems bring the opportunity to develop new applications to solve these problems. In particular, Smart Cities greatly enrich their sustainability by introducing new resource management applications that rely in those constrained devices a significant part of the functionality of the system. The proposed Smart Parking solution consists mainly in the on-site deployment of an IoT solution to monitor and signalize the state of availability of each single parking space, as well as using context information generated by the city and its citizens to provide accurate responses to driver's demands. Furthermore, this system improves the management of parking resources by public authorities, for instance handling groups of parking spaces facilitating the whole city traffic management. The integration of this deployment into an existing live test-bed implies an easy task requiring just the data collection through the available means of the parking spaces availability. At the present time there exist living test-beds which can be used to integrate these new functionalities for experimentation on IoT data level, to gain a better knowledge and understanding of the M2M world, reducing costs, resources, pollution and time.
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