AI交通科学家:大模型驱动的自主交通科研OA

Urban transportation systems are rapidly evolving into CPSS(cyber-physical-social system),driven by the con-tinuous 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 para-digms 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 opti-mization—the framework enabled the autonomous discovery of transportation laws and the continuous evolution of con-trol strategies.This research establishes a scalable technical paradigm for advancing transportation science within CPSS environments,ensuring both efficient problem-solving and innovative strategy iteration.