用大模型优化公交系统，提升出行体验
Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study
- 用大模型分析公交数据，智能规划路线
- 不同ChatGPT模型对比显示可有效理解交通信息
- 适合城市交通管理者与智能出行研究者
将大型语言模型（LLMs）融入公共交通系统，有望显著提升城市出行效率。本研究以圣安东尼奥市公交系统为案例，探索LLMs在自然语言处理与数据分析方面的潜力，评估其在优化线路规划、缩短候车时间及提供个性化出行帮助方面的应用。基于通用交通数据规范（GTFS）及其他相关数据，研究旨在验证LLMs能否改善资源分配、提升乘客满意度，并支持数据驱动的运营决策。通过对比不同ChatGPT模型在理解交通信息、检索数据和生成完整回答方面的能力，结果表明：尽管LLMs具有巨大潜力，但需精细调优才能充分发挥作用。该研究为其他城市构建智能化公交系统提供了实践参考。
原文摘要 · Abstract (English)
The integration of large language models (LLMs) into public transit systems presents a transformative opportunity to enhance urban mobility. This study explores the potential of LLMs to revolutionize public transportation management within the context of San Antonio's transit system. Leveraging the capabilities of LLMs in natural language processing and data analysis, we investigate their capabilities to optimize route planning, reduce wait times, and provide personalized travel assistance. By utilizing the General Transit Feed Specification (GTFS) and other relevant data, this research aims to demonstrate how LLMs can potentially improve resource allocation, elevate passenger satisfaction, and inform data-driven decision-making in transit operations. A comparative analysis of different ChatGPT models was conducted to assess their ability to understand transportation information, retrieve relevant data, and provide comprehensive responses. Findings from this study suggest that while LLMs hold immense promise for public transit, careful engineering and fine-tuning are essential to realizing their full potential. San Antonio serves as a case study to inform the development of LLM-powered transit systems in other urban environments.
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