Household Travel Survey

Household travel surveys are crucial for understanding population movement patterns, informing urban planning, and improving transportation systems. Current research focuses on enhancing data collection and analysis through innovative techniques like large language models (LLMs) to synthesize survey data and deep learning algorithms to predict travel behavior with greater accuracy. These advancements address challenges such as data scarcity, privacy concerns, and the limitations of traditional modeling approaches, ultimately leading to more efficient and sustainable transportation solutions. The resulting insights are valuable for both academic research and practical applications in urban planning, transportation policy, and resource allocation.