Travel Time

Travel time estimation and optimization are crucial for efficient transportation systems, aiming to accurately predict travel times and design networks minimizing overall travel duration and promoting fairness. Current research heavily utilizes graph neural networks (GNNs), often integrated with reinforcement learning or Monte Carlo Tree Search, to model complex spatial and temporal dependencies in traffic flow and route selection, incorporating stochasticity and uncertainty. These advancements improve the accuracy and efficiency of travel time prediction, impacting applications such as navigation apps, traffic management, and the design of public transportation networks.