Intelligent Deep Learning Framework for Traffic Congestion Prediction in Internet of Vehicles Using Bi-LSTM and TabTransformer

One of the main components of Intelligent Transportation Systems (ITS) is traffic congestion predictions. Traffic congestion in metropolitan road networks has a substantial impact on sustainability. Effective congestion management lowers prolonged travel delays and pollution emissions. However, because traffic patterns are complicated, dynamic, and non-linear, accurately predicting the spread of congestion is still difficult. With the introduction of Internet of Things (IoT) devices, useful datasets that can aid in the creation of sustainable and intelligent transportation for contemporary cities have been made available. This work presents an Intelligent Deep Learning Framework that combines Bi-LSTM with the TabTransformer architecture for congestion prediction in the Internet of Vehicles (IoV). The Bi-LSTM enhances temporal modeling of traffic flow dynamics, and the TabTransformer employs self-attention to derive high-quality feature representations. The proposed framework is tested on a large-scale dataset of over 317,112 traffic instances, incorporating a wide range of contextual features such as time indicators, route characteristics, and environmental factors. The model is validated using stratified k-fold cross-validation, and class imbalance is handled by class weighting in each fold. Experiential evaluation indicates that the proposed hybrid framework achieves 99.52% accuracy, outperforming several baseline models, including Decision Tree (92%), Extra Trees (94%), LSTM (86.94%), GRU (87.09%), Bi-LSTM (87.77%), CNN-LSTM (98.41%), and TabTransformer (97.32%). Compared to the existing algorithms, the proposed work outperformed with the parameters MSE, MAE, and R 2 . The better performance exhibits that BiLSTM with Tab Transformer is well-suited to predict congestion patterns in road networks.