Route Choice

Route choice research investigates how individuals and autonomous systems select paths within transportation networks, aiming to optimize travel time, comfort, or other objectives. Current research focuses on developing sophisticated models, including deep reinforcement learning and inverse reinforcement learning, to understand and predict route choices based on diverse factors like individual preferences (e.g., visual appeal for cyclists, amenity access for seniors), autonomous vehicle coordination, and real-time traffic conditions. These advancements are crucial for improving urban planning, optimizing transportation systems (e.g., efficient delivery routes), and developing more effective navigation tools tailored to specific user needs. The ultimate goal is to create more efficient, user-friendly, and sustainable transportation networks.