ABSTRACT

Nowadays, urban traffic affects the quality of life in cities as the problem becomes even more exacerbated by parking issues: congestion increases due to drivers searching slots to park. An Internet of Things approach permits drivers to know the parking availability in real time and provides data that can be used to develop predictive models. This can be useful in improving the management of parking areas while having an important effect on traffic. This work begins by describing the state-of-the-art parking predictive models and, then, introduces the recurrent neural network methods that were used Long Short-Term Memory and Gated Recurrent Unit, as well as the models developed according to real scenarios in Wattens and Los Angeles. To improve the quality of the models, exogenous variables related to weather and calendar are considered. Finally, the results are described, followed by suggestions for future research.

抽象的

如今，由于停车问题使问题变得更加严重，城市交通影响着城市的生活质量：由于驾驶员寻找停车位而导致交通拥堵加剧。物联网方法允许驾驶员实时了解停车位的可用性，并提供可用于开发预测模型的数据。这对改善停车区的管理非常有用，同时又对交通产生重要影响。这项工作首先描述了最新的停车预测模型，然后介绍了使用长短期记忆和门控循环单元的循环神经网络方法，以及根据实际场景开发的模型。瓦滕斯和洛杉矶。为了提高模型的质量，考虑了与天气和日历有关的外生变量。最后，