Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis

• URL: http://arxiv.org/abs/2507.10382v2
• Date: Wed, 23 Jul 2025 10:02:51 GMT
• Title: Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis
• Authors: Yue Ding, Conor McCarthy, Kevin O'Shea, Mingming Liu,
• Abstract summary: We present a cloud-based, LLM-powered shared e-mobility platform, integrated with a mobile application for personalized route recommendations.<n>The system achieves an average execution accuracy of 0.81 on system operator queries and 0.98 on user queries.
• Score: 1.7521077353162031
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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