用大模型+检索增强生成，让城市共享电动车更智能。
Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis
- 构建云端大模型平台，结合移动应用实现个性化路线推荐。
- 不同交通场景下优化出行时间和成本，效果显著提升。
- 检索增强框架在用户查询上准确率达98%，适合交通系统开发者。
随着智慧出行和共享电动出行服务的发展，诸多先进技术被引入该领域。基于云的交通仿真解决方案蓬勃发展，日益呈现逼真的出行图景。大语言模型（LLMs）作为创新工具，在智能决策、用户交互和实时交通分析中展现出强大支持能力。随着用户对电动出行需求持续增长，提供端到端的完整解决方案变得至关重要。本文提出一个基于云的大模型驱动共享电动出行平台，集成移动端应用实现个性化路线推荐。优化模块在不同交通场景下以出行时间和成本为指标进行评估。此外，采用多种评估方法对大模型驱动的RAG框架在模式层面的表现进行了测试。基于XiYanSQL的模式级RAG在系统操作类查询上平均执行准确率为0.81，在用户查询上达到0.98。
原文摘要 · Abstract (English)
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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