AI transportation scientist: LLM-driven autonomous transportation research

Urban transportation systems are rapidly evolving into CPSS (cyber-physical-social system), driven by the continuous integration of autonomous vehicles, unmanned aerial vehicles, and diverse intelligent agents. This evolution has dramatically increased system complexity, dynamics, and coupling, rendering traditional human-centric research paradigms insufficient for timely understanding and response to fast-evolving system behaviors. To address these challenges, an autonomous framework called "AI Transportation Scientist" was proposed to revolutionize transportation research through parallel intelligence. The architecture leveraged a synergy between large language model and multi-agent system across four functional layers (interaction, cognitive, experimental, and support). At its core, a dynamic routing engine adaptively scheduled intelligent agents to tackle mechanism discovery, strategy validation, and system optimization. By implementing a full-chain collaborative closed loop—encompassing problem identification, simulation, and feedback optimization—the framework enabled the autonomous discovery of transportation laws and the continuous evolution of control strategies. This research establishes a scalable technical paradigm for advancing transportation science within CPSS environments, ensuring both efficient problem-solving and innovative strategy iteration.