mlsanityData to 14 Oct 2026

TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation

arXiv 2411.16020 ↗First posted 25 Nov 2024cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 25 Nov 2024.

Builds on

13 of 29 references.

References · 13

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1 more
  1. 2018SolarGest: Ubiquitous and Battery-free Gesture Recognition using Solar Cellscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  2. 2025Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Reviewcited by 8
  3. 2025LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioningcited by 2
  4. 2026Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Supportcited by 0