Intelligent transportation systems (ITSs) are revolutionizing transportation by integrating advanced technologies to optimize efficiency, safety, and sustainability. Traffic prediction, a core component of ITS is quite important in improving traffic management, enhancing public safety, and promoting sustainable urban mobility. However, deep learning models’ efficacy in traffic prediction is heavily reliant on the quality of input data. Data noise, biases, and missing values can significantly hinder model accuracy by introducing spurious correlations that do not reflect true causal relationships. Additionally, imbalanced training data can skew the model’s learning process, leading to suboptimal performance and reduced generalization. Traditional feature selection methods often find it difficult to depict intricate, nonlinear interactions in high-dimensional datasets, further complicating the task. To overcome these obstacles, this research proposes a novel graph-GRU temporal fusion network (GGTFN) that combines graph neural networks (GNNs) and gated recurrent units (GRUs) to capture both spatial and temporal dependencies in traffic data. The model also incorporates regression-based imputation during preprocessing to handle missing data and uses SMOTE-NC to address class imbalance. Experimental findings indicate that the GGTFN performs better than other previous techniques, achieving the lowest RMSE values for short- to medium-term traffic predictions (15–30 min). Although performance decreases for longer prediction horizons, the proposed model demonstrates robustness and effective generalization across diverse traffic conditions. This research sets the stage for more precise and effective traffic management, helping to the creation of more intelligent and environmentally friendly transportation networks.
Pallavi et al. (Sat,) studied this question.