An Integrated Framework of GPT-4 and PINN for Dynamic Traffic Estimation and Support

This research presents a groundbreaking approach to enhance traffic guidance systems by integrating GPT-4 with the Physics-Informed Neural Network-based traffic state estimator (PINN-TSE). Unlike traditional systems, our proposed method minimizes reliance on live data, ensuring robust service delivery despite data gaps. The PINN-TSE model demonstrates high precision with a Mean Absolute Error of less than 4 vehicles/mile in traffic density estimation, even in data-scarce regions. Results highlight its reliability in providing accurate traffic information, especially in areas with sparse conventional sensors or potential data interruptions. Additionally, GPT-4 enhances user interactions, offering not only precise updates but also personalized experiences by understanding and responding to user inquiries. This AI-integrated traffic guidance system surpasses traditional methods in estimation, personalization, and reliability, paving the way for smarter traffic management.