Data on the Move: Traffic-Oriented Data Trading Platform Powered by AI Agent with Common Sense
<|reference_start|>Data on the Move: Traffic-Oriented Data Trading Platform Powered by AI Agent with Common Sense: In the digital era, data has become a pivotal asset, advancing technologies such as autonomous driving. Despite this, data trading faces challenges like the absence of robust pricing methods and the lack of trustworthy trading mechanisms. To address these challenges, we introduce a traffic-oriented data trading platform named Data on The Move (DTM), integrating traffic simulation, data trading, and Artificial Intelligent (AI) agents. The DTM platform supports evident-based data value evaluation and AI-based trading mechanisms. Leveraging the common sense capabilities of Large Language Models (LLMs) to assess traffic state and data value, DTM can determine reasonable traffic data pricing through multi-round interaction and simulations. Moreover, DTM provides a pricing method validation by simulating traffic systems, multi-agent interactions, and the heterogeneity and irrational behaviors of individuals in the trading market. Within the DTM platform, entities such as connected vehicles and traffic light controllers could engage in information collecting, data pricing, trading, and decision-making. Simulation results demonstrate that our proposed AI agent-based pricing approach enhances data trading by offering rational prices, as evidenced by the observed improvement in traffic efficiency. This underscores the effectiveness and practical value of DTM, offering new perspectives for the evolution of data markets and smart cities. To the best of our knowledge, this is the first study employing LLMs in data pricing and a pioneering data trading practice in the field of intelligent vehicles and smart cities.<|reference_end|>
@article{yu2024data,
    title={Data on the Move: Traffic-Oriented Data Trading Platform Powered by AI
  Agent with Common Sense},
    author={Yi Yu, Shengyue Yao, Tianchen Zhou, Yexuan Fu, Jingru Yu, Ding Wang,
  Xuhong Wang, Cen Chen, Yilun Lin},
    journal={arXiv preprint arXiv:2407.00995},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00995},
    primaryClass={cs.CY cs.SY eess.SY physics.app-ph}
}