Estimated Time of Arrival

Accurate Estimated Time of Arrival (ETA) prediction is crucial for optimizing transportation systems and various location-based services. Current research focuses on improving ETA accuracy using advanced machine learning techniques, including ensemble methods combining tree-based models and neural networks, graph neural networks leveraging road network topology and hierarchical structures, and post-processing systems that refine initial ETA estimates. These advancements are significantly impacting real-world applications, such as navigation systems, ride-hailing services, and logistics, by enhancing efficiency and user experience.