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.
翻译：A.I.干扰和创新竞争的必要性正在影响那些越来越有必要成为创新热点的城市。然而,如果没有证明的解决办法,就需要实验,而且往往不成功。 但是,在城市进行实验不仅对其公民产生许多不良效果,而且如果失败也会产生声誉。 数字双体在其他领域非常流行,似乎是扩大实验提案的有希望的方法,但在模拟环境中却如此流行,只将半发性、成功概率较高的双体转化为真实环境,从而将风险降到最低。 然而,数字双体是数据密集型的,需要高度本地化的数据,使其难以扩大规模,特别是小城市,而且数据收集的成本也很高。 我们提出了基于合成数据的替代方法,该方法提供了在智能城市非常常见的一些条件,可以解决这两个问题,同时提供基于二氧化氮污染的证明概念。
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