Data on the Move: Traffic-Oriented Data Trading Platform Powered by AI Agent with Common Sense
Yi Yu, Shengyue Yao, Tianchen Zhou, Yexuan Fu, Jingru Yu, Ding Wang,, Xuhong Wang, Cen Chen, Yilun Lin

TL;DR
This paper introduces Data on The Move (DTM), an AI-powered traffic data trading platform that uses large language models for data valuation and trading, improving market efficiency and supporting smart city development.
Contribution
It is the first to employ LLMs for data pricing and to implement a traffic-oriented data trading platform integrating traffic simulation and AI agents.
Findings
AI-based pricing improves data market rationality
Simulation demonstrates enhanced traffic efficiency
First use of LLMs in traffic data trading
Abstract
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…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation
