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

• DOI: 10.1016/j.inffus.2024.102466
• Publication Date: 2024-05-11T05:51:34Z
• Authors: Shams Forruque Ahmed; Sweety Angela Kul...; Sabiha Jannat Rafa; Javeria Fazal; Mahfara Hoque; Gang Liu; Amir H. Gandomi
• Keywords: Sensor Fusion

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