用大模型压缩交通传感器数据，提升存储与分析效率
TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation
- 用大模型理解多模态交通数据，实现智能压缩
- 在不同压缩比下仍能高精度还原气压、速度等数据
- 适合智能交通系统中的数据存储优化场景
将大语言模型（LLMs）引入智能交通系统，可显著提升数据管理与运营效率。本文提出TransCompressor框架，利用LLMs对多模态交通传感器数据进行高效压缩与解压缩。该框架在公交车、出租车、MTR等多种交通模式下，针对气压、速度、海拔等不同类型传感器数据进行了全面评估。实验结果表明，在不同压缩比条件下，TransCompressor能有效重建原始数据。研究表明，通过精心设计的提示词，LLMs可凭借其丰富的知识库参与数据压缩过程，从而提升智能交通环境中数据的存储、分析与检索能力。
原文摘要 · Abstract (English)
The incorporation of Large Language Models (LLMs) into smart transportation systems has paved the way for improving data management and operational efficiency. This study introduces TransCompressor, a novel framework that leverages LLMs for efficient compression and decompression of multimodal transportation sensor data. TransCompressor has undergone thorough evaluation with diverse sensor data types, including barometer, speed, and altitude measurements, across various transportation modes like buses, taxis, and MTRs. Comprehensive evaluation illustrates the effectiveness of TransCompressor in reconstructing transportation sensor data at different compression ratios. The results highlight that, with well-crafted prompts, LLMs can utilize their vast knowledge base to contribute to data compression processes, enhancing data storage, analysis, and retrieval in smart transportation settings.
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