中文标题#
利用 RAG-LLMs 进行城市交通模拟与分析
英文标题#
Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis
中文摘要#
随着智能出行和共享电动出行服务的兴起，许多先进技术已被应用于这一领域。 基于云的交通仿真解决方案蓬勃发展，提供了对不断变化的出行环境越来越真实的表示。 大语言模型（LLMs）已成为先驱工具，为各种应用提供了强大的支持，包括智能决策、用户交互和实时交通分析。 随着用户对电动出行需求的持续增长，提供全面的端到端解决方案变得至关重要。 在本文中，我们提出了一种基于云的、由大语言模型驱动的共享电动出行平台，并集成了移动应用程序以提供个性化的路线推荐。 优化模块根据不同交通场景下的行驶时间和成本进行评估。 此外，使用不同的评估方法，在模式级别对基于大语言模型的 RAG 框架进行了评估，针对不同用户。 模式级别的 RAG 与 XiYanSQL 在系统操作符查询上的平均执行准确率为 0.81，在用户查询上的平均执行准确率为 0.98。
英文摘要#
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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