DynaGraph: Towards Dynamic Graph Learning for Multi-scale Traffic Generation with Spatial-temporal Agent Framework

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 DynaGraph, using a multi-agent framework to process multi-scale traffic data, conduct multi-scale traffic analysis, and present multi-scale visualization results. DynaGraph consists of three essential AI agents: 1) a text-to-demand agent with deep thinking ability to interact with users and extract prediction tasks through texts or voice; 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 DynaGraph as a generic system focuses on addressing concerns about traffic prediction from transportation participants, and conducted extensive experiments on five real-world road datasets to demonstrate its competitive prediction accuracy, scalability, and superior interactive performance.