Travel Behavior

Travel behavior research aims to understand how individuals and populations move, focusing on predicting and influencing choices related to transportation modes and routes. Current research heavily utilizes machine learning, particularly deep learning models and large language models (LLMs), often integrated with agent-based modeling and incorporating diverse data sources like GPS tracking, surveys, and satellite imagery to improve prediction accuracy and model interpretability. These advancements are crucial for optimizing transportation systems, developing effective transport policies (especially in developing countries), and mitigating environmental impacts like carbon emissions through improved urban planning and sustainable transportation choices.