Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promising, indeed. However, existing approaches are limited by the high cost of sensors that need to be installed throughout the city in order to achieve an accurate estimation. This work investigates the extension of estimating parking information from areas equipped with sensors to areas where they are missing. To this end, the similarity between city neighborhoods is determined based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas.

中文翻译：

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将知识从监控区域转移到非监控区域以预测停车位

世界各地的智能城市已经开始监控停车区，以估计可用停车位并帮助司机寻找停车位。目前的结果确实很有希望。然而，现有方法受限于需要在整个城市安装以实现准确估计的传感器的高成本。这项工作调查了将估计停车信息的区域从配备传感器的区域扩展到缺少传感器的区域。为此，城市邻里之间的相似性是基于背景数据，即来自地理信息系统的数据来确定的。使用派生的相似性值，我们分析了从受监控停车区到未监控停车区的占用率的适应情况。