Enhancement of traffic forecasting through graph neural network-based information fusion techniques

To improve forecasting accuracy and capture intricate interactions within transportation networks, information fusion approaches are crucial for traffic predictions based on graph neural networks (GNNs). GNNs offer a potentially effective framework for capturing intricate patterns and interactions among diverse elements, such as road segments and crossings, by considering both temporal and geographical dependencies. Although GNN-based traffic forecasting has recently been investigated in many studies, there is a need for comprehensive reviews that examine information fusion approaches for GNN-based traffic predictions, including an analysis of their benefits and challenges. This study addresses this knowledge gap and offers future insights into the potential advancements and developing fields of research in GNN-based fusion techniques, as well as their implications in urban planning and smart cities. Existing research demonstrates that the accuracy of traffic forecasting is substantially enhanced by information fusion techniques based on GNNs in comparison to more conventional approaches. By integrating information fusion methods with GNNs, the model is capable of capturing intricate temporal and spatial relationships between various locations in a traffic network. Multi-source data integration benefits traffic forecasting models, including social events, weather conditions, real-time traffic sensor data, and historical traffic patterns. In addition, combining GNNs with other AI methods like evolutionary algorithms or reinforcement learning could be an efficient strategy. With the potential to combine the best features of several methods, hybrid models could improve overall performance and flexibility in challenging traffic situations.