TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation
This addresses data storage and analysis challenges in smart transportation systems, but it is incremental as it applies existing LLM capabilities to a new domain.
The study tackled the problem of managing multimodal sensor data in smart transportation by introducing TransCompressor, an LLM-powered framework for compression and decompression, which effectively reconstructed data at various compression ratios.
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.