Enhanced congestion prediction of traffic flow using a hybrid attention-based deep learning model

Traffic congestion has become a critical issue worldwide, significantly impacting the quality of life and causing economic losses in urban areas. To address this, the concept of smart cities has emerged, utilizing technologies like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) to optimize transportation systems. One of the main challenges in smart city traffic management is accurately predicting traffic congestion. Existing approaches often struggle with providing reliable predictions, especially in cloud-based environments, which can lead to inaccurate traffic estimates. This article, propose a novel data-driven approach to predict traffic congestion in smart cities using a hybrid model that combines bi-directional long short-term memory (Bi-LSTM), convolutional neural networks (CNN), and an attention network. This model leverages factors such as traffic conditions, road types, and weather data to improve prediction accuracy. The model’s performance is evaluated using key metrics including root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE), across four junctions (J1, J2, J3, J4). The proposed model consistently outperforms existing methods, including gated recurrent unit (GRU), long short-term memory (LSTM), CNN, and multi-layer perceptron (MLP). In Junction 1, the proposed model achieved the best results with RMSE = 0.252, MSE = 0.063, and MAE = 0.178. In Junction 2, the proposed model again led with RMSE = 0.561, while other models, like GRU and LSTM, showed higher error rates. These results confirm that the hybrid model is more effective at predicting traffic congestion, particularly in dynamic urban environments. The proposed approach offers a promising solution for improving traffic flow and optimizing smart city infrastructure.