Personal Mobility Generation

Personal mobility generation research focuses on computationally modeling individual travel patterns, aiming to create realistic and personalized simulations of human movement. Current approaches leverage large language models (LLMs) within agent-based frameworks, incorporating factors like individual preferences, social norms, and contextual information to generate more accurate mobility trajectories. These models are being evaluated and refined using real-world mobility data, and their improved accuracy has implications for urban planning, traffic management, and public health research. Furthermore, research is exploring the use of exoskeletons and autonomous driving systems to enhance personal mobility, particularly for individuals with mobility impairments.