This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real world traffic pattern dataset and compared with existing classification methods.

本研究提出了一种使用深度递归神经网络来分类智能城市交通模式的新方法，该方法可以有效捕捉交通模式的动态和时序特征，实验证明该模型在准确度、精确度、召回率和F1得分等方面优于现有方法，并对结果进行深入分析并讨论了其对智能城市的影响，结果显示该模型能以高达95%的精确度准确分类智能城市的交通模式，同时在真实的交通模式数据集上评估了该模型并与现有分类方法进行了比较。

智慧城市中使用深度循环神经网络进行交通模式分类