Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis
This work addresses the need for comprehensive end-to-end solutions in smart mobility for users and operators, but it appears incremental as it integrates existing technologies like LLMs and RAG into a specific domain.
The paper tackles the problem of improving urban mobility by developing a cloud-based, LLM-powered shared e-mobility platform with personalized route recommendations, achieving an average execution accuracy of 0.81 on system operator queries and 0.98 on user queries using a schema-level RAG framework.
With the rise of smart mobility and shared e-mobility services, numerous advanced technologies have been applied to this field. Cloud-based traffic simulation solutions have flourished, offering increasingly realistic representations of the evolving mobility landscape. LLMs have emerged as pioneering tools, providing robust support for various applications, including intelligent decision-making, user interaction, and real-time traffic analysis. As user demand for e-mobility continues to grow, delivering comprehensive end-to-end solutions has become crucial. In this paper, we present a cloud-based, LLM-powered shared e-mobility platform, integrated with a mobile application for personalized route recommendations. The optimization module is evaluated based on travel time and cost across different traffic scenarios. Additionally, the LLM-powered RAG framework is evaluated at the schema level for different users, using various evaluation methods. Schema-level RAG with XiYanSQL achieves an average execution accuracy of 0.81 on system operator queries and 0.98 on user queries.