Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to “sensitize” infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of Things (IoT) sensors located in the parking areas or the underground car parks in combination with a mobile application, which indicates to citizens the free places in the different areas of the city and guides them toward the chosen parking, it is possible to reduce air pollution and fluidifying noise traffic. In this article, we present and discuss an innovative Deep Learning-based ensemble technique in forecasting the parking space occupancy to reduce the search time for parking and to optimize the flow of cars in particularly congested areas, with an overall positive impact on traffic in urban centres. A genetic algorithm has also been used to optimize predictors parameters. The main goal is to design an intelligent IoT-based service that can predict, in the next few hours, the parking spaces occupancy of a street. The proposed approach has been assessed on a real IoT dataset composed by over than 15M of collected sensor records. Obtained results demonstrate that our method outperforms both single predictors and the widely used strategy of the mean providing inherently robust predictions.

中文翻译：

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智能停车的预测分析：物联网数据预测中的深度学习方法

如今，可持续的智慧城市专注于能源效率和通过智能移动项目和“提高”基础设施的举措减少污染排放。智能停车是智能移动的基石之一，创新的移动旨在灵活、集成和可持续，从而融入智慧城市。通过使用位于停车场或地下停车场的物联网 (IoT) 传感器与移动应用程序相结合，向市民指示城市不同区域的空闲位置并引导他们前往选择的停车场，有可能减少空气污染和流化噪音交通。在本文中，我们提出并讨论了一种创新的基于深度学习的集成技术，用于预测停车位占用率，以减少停车搜索时间并优化特别拥挤区域的汽车流量，从而对城市中心的交通产生总体积极影响。遗传算法也已用于优化预测参数。主要目标是设计一种基于物联网的智能服务，可以在接下来的几个小时内预测街道的停车位占用情况。所提出的方法已经在由超过 1500 万个收集的传感器记录组成的真实物联网数据集上进行了评估。获得的结果表明，我们的方法优于单一预测变量和广泛使用的均值策略，提供固有的稳健预测。