  The precise prediction of multi-scale traffic is a ubiquitous challenge in
the urbanization process for car owners, road administrators, and governments.
In the case of complex road networks, current and past traffic information from
both upstream and downstream roads are crucial since various road networks have
different semantic information about traffic. Rationalizing the utilization of
semantic information can realize short-term, long-term, and unseen road traffic
prediction. As the demands of multi-scale traffic analysis increase, on-demand
interactions and visualizations are expected to be available for transportation
participants. We have designed a multi-scale traffic generation system, namely
TrafficGPT, using three AI agents to process multi-scale traffic data, conduct
multi-scale traffic analysis, and present multi-scale visualization results.
TrafficGPT consists of three essential AI agents: 1) a text-to-demand agent
that is employed with Question & Answer AI to interact with users and extract
prediction tasks through texts; 2) a traffic prediction agent that leverages
multi-scale traffic data to generate temporal features and similarity, and fuse
them with limited spatial features and similarity, to achieve accurate
prediction of three tasks; and 3) a suggestion and visualization agent that
uses the prediction results to generate suggestions and visualizations,
providing users with a comprehensive understanding of traffic conditions. Our
TrafficGPT system focuses on addressing concerns about traffic prediction from
transportation participants, and conducted extensive experiments on five
real-world road datasets to demonstrate its superior predictive and interactive
performance
