This research explores how large language models can enhance transportation systems through advanced mobility analysis and forecasting capabilities.

• Addresses the growing conflict between transportation demands and infrastructure limitations
• Enables more accurate prediction of human travel patterns for urban management
• Leverages machine learning flexibility to improve traffic forecasting accuracy
• Provides critical insights for taxi dispatch, urban planning, and infrastructure optimization

For engineers, this research offers powerful new tools to anticipate transportation needs, optimize resource allocation, and develop more responsive infrastructure systems—ultimately creating smarter, more efficient cities.

Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks