论文标题
使用合成数据来解决智能城市数字双胞胎中的可扩展性和数据可用性问题
The use of Synthetic Data to solve the scalability and data availability problems in Smart City Digital Twins
论文作者
论文摘要
AI。破坏和创新竞争的需求正在影响成为创新热点有必要的城市。但是，没有经过验证的解决方案，通常需要实验，通常不成功。但是，在城市中进行的实验不仅对其公民产生了许多不良影响，而且如果失败的话，也有声誉。在其他领域如此受欢迎的数字双胞胎似乎是扩展实验建议的一种有希望的方法，但是在模拟环境中，只能翻译出半熟的人，即成功的可能性较高的人，将其转化为真实的环境，从而最大程度地减少风险。但是，数字双胞胎是数据密集的，需要高度局部的数据，使其难以扩展，尤其是对于小城市而言，并且与数据收集相关的高成本。我们提出了一种基于合成数据的替代方案，该替代方案给定在智能城市中很常见的某些条件，可以解决这两个问题以及基于NO2污染的概念验证。
The A.I. disruption and the need to compete on innovation are impacting cities that have an increasing necessity to become innovation hotspots. However, without proven solutions, experimentation, often unsuccessful, is needed. But experimentation in cities has many undesirable effects not only for its citizens but also reputational if unsuccessful. Digital Twins, so popular in other areas, seem like a promising way to expand experimentation proposals but in simulated environments, translating only the half-baked ones, the ones with higher probability of success, to real environments and therefore minimizing risks. However, Digital Twins are data intensive and need highly localized data, making them difficult to scale, particularly to small cities, and with the high cost associated to data collection. We present an alternative based on synthetic data that given some conditions, quite common in Smart Cities, can solve these two problems together with a proof-of-concept based on NO2 pollution.
