Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis

TL;DR

This paper introduces a cloud-based, LLM-powered shared e-mobility platform that enhances urban traffic simulation, decision-making, and user interaction through a RAG framework, demonstrating high accuracy in query execution.

Contribution

It presents a novel integration of RAG-LLMs into urban mobility simulation, optimizing travel and evaluating schema-level performance for personalized e-mobility solutions.

Findings

Schema-level RAG with XiYanSQL achieves 0.81 accuracy on system queries.

Achieves 0.98 accuracy on user queries.

Demonstrates effective real-time traffic analysis and decision support.

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.