TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation

TL;DR

TransCompressor is a novel framework that uses Large Language Models to efficiently compress and decompress multimodal transportation sensor data, improving data management in smart transportation systems.

Contribution

It introduces a new LLM-based approach for multimodal data compression in transportation, demonstrating its effectiveness across various sensor types and transportation modes.

Findings

Effective data reconstruction at various compression ratios.

LLMs can leverage prompts to assist in data compression.

Enhanced storage and retrieval of transportation data.

Abstract

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.