OD Matrix

Origin-destination (OD) matrices represent the flow of entities (e.g., vehicles, dust particles) between different locations, a crucial element in diverse fields like transportation and atmospheric science. Current research focuses on improving OD matrix estimation accuracy and efficiency, particularly using deep learning architectures such as neural networks with attention mechanisms and graph-based models to address the inherent underdetermination and temporal complexities of the problem. These advancements offer significant potential for enhancing traffic management, weather forecasting, and other applications requiring accurate real-time estimations of movement patterns.