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.

翻译：将大语言模型（LLMs）融入智能交通系统为改善数据管理与运营效率开辟了新途径。本研究提出TransCompressor——一种利用LLMs实现多模态交通传感器数据高效压缩与解压的创新框架。该框架已在公交车、出租车和地铁等多种交通模式下，针对气压计、速度和海拔高度等不同类型的传感器数据进行了全面评估。综合测试表明，TransCompressor在不同压缩比下均能有效重构交通传感器数据。研究结果凸显：通过精心设计的提示，LLMs能够运用其庞大的知识库参与数据压缩过程，从而提升智能交通场景下的数据存储、分析与检索能力。