TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation
Authors: 
Huanqi Yang, Rucheng Wu, Weitao XuAuthors Info & Claims
Huanqi Yang, Rucheng Wu, Weitao XuAuthors Info & ClaimsPages 2335 - 2340
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 Mass Transit Railways (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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Index Terms
- TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation
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Information & Contributors
Information
Published In
December 2024
2476 pages
ISBN:9798400704895
DOI:10.1145/3636534
- Chair:
- Weisong Shi,
- Program Chairs:
- Deepak Ganesan,
- Nicholas Lane
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Publication History
Published: 04 December 2024
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- Research-article
Conference
ACM MobiCom '24
Sponsor:
ACM MobiCom '24: 30th Annual International Conference on Mobile Computing and Networking
November 18 - 22, 2024
DC, Washington D.C., USA
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Overall Acceptance Rate 440 of 2,972 submissions, 15%
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