Transforming Urban Dynamics: Harnessing Large Language Models for Smarter Mobility

Artificial intelligence (AI) has the potential to analyze mobility data and make mobility systems smarter by leveraging diverse data sources such as geospatial data, transportation logs, and real-time sensor data to optimize traffic flow, enhance public transportation systems, and support the development of autonomous vehicles. With the newly emerged generative AI paradigm, exemplified by large language models (LLMs), there is great potential to transform the current AI applications in mobility, transportation, and urban domains. This article provides an overview of recent efforts and aims to shed light on the challenges and future opportunities to facilitate the adaptation of LLMs for smarter mobility systems.

Authors

• Hao Xue
• Ming Jin Virginia Tech
• Shirui Pan Griffith University, Queensland, Australia
• Flora Salim

Keywords

• Generative AI
• Large language models
• Soft sensors
• Transforms
• Real-time systems
• Geospatial analysis
• Vehicle dynamics
• Intelligent systems
• Public transportation
• Autonomous vehicles
• Urban Dynamics
• Transport System
• Urban Planning
• Information And Communication Technologies
• Traffic Congestion
• Traffic Flow
• Mobile Data
• Generalization Capability
• Mobile System
• Urban System
• Smart City
• Artificial Intelligence Applications
• Variety Of Data Sources
• Urban Mobility
• Mobility Domain
• Smart Mobile
• Global Urbanization
• Graph-structured Data
• Edge Devices
• Urban Development
• Big Tech
• Mobility Scenarios
• Urban Governance
• Traffic Control
• Traffic Management
• Light Signal
• Urban Environments