Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network
This work addresses traffic management challenges for smart city planners and operators, but it is incremental as it applies a hybrid deep learning method to an existing domain.
This paper tackled traffic pattern classification in smart cities by proposing a deep recurrent neural network model that combines convolutional and recurrent layers, achieving a precision of up to 95% and outperforming existing methods in accuracy, precision, recall, and F1 score.
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.