Route Planning

Route planning optimizes the selection of paths between origins and destinations, aiming to minimize travel time, cost, or other relevant metrics. Current research emphasizes improving efficiency and accuracy through various techniques, including reinforcement learning (especially multi-agent approaches addressing asynchronous decision-making), graph-based algorithms (like Floyd-Warshall and Ant Colony Optimization), and data-driven models using neural networks and trajectory data mining. These advancements have significant implications for various applications, such as optimizing last-mile delivery, improving navigation in challenging environments (like polar regions or disaster zones), and enhancing the safety and efficiency of multi-modal transportation systems.