AI-Integrated Traffic Information System: A Synergistic Approach of Physics Informed Neural Network and GPT-4 for Traffic Estimation and Real-Time Assistance

Traffic management systems have primarily relied on live traffic sensors for real-time traffic guidance. However, this dependence often results in uneven service delivery due to the limited scope of sensor coverage or potential sensor failures. This research introduces a novel approach to overcome this limitation by synergistically integrating a Physics-Informed Neural Network-based Traffic State Estimator (PINN-TSE) with a powerful Natural Language Processing model, GPT-4. The purpose of this integration is to provide a seamless and personalized user experience, while ensuring accurate traffic density prediction even in areas with limited data availability. The innovative PINN-TSE model was developed and tested, demonstrating a promising level of precision with a Mean Absolute Error of less than four vehicles per mile in traffic density estimation. This performance underlines the model’s ability to provide dependable traffic information, even in regions where conventional traffic sensors may be sparsely distributed or data communication is likely to be interrupted. Furthermore, the incorporation of GPT-4 enhances user interactions by understanding and responding to inquiries in a manner akin to human conversation. This not only provides precise traffic updates but also interprets user intentions for a tailored experience. The results of this research showcase an AI-integrated traffic guidance system that outperforms traditional methods in terms of traffic estimation, personalization, and reliability. While the study primarily focuses on a single road segment, the methodology shows promising potential for expansion to network-level traffic guidance, offering even greater accuracy and usability. This paves the way for a smarter and more efficient approach to traffic management in the future.