  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.
