Real-Time Traffic Insights with Physics-Informed Neural Networks: Integrating the Aw-Rascle Model and LLMs

Traffic congestion and inefficiencies in transportation networks pose significant challenges to road safety, travel times, and environmental sustainability. Traditional traffic management systems, typically reliant on sparse sensor data and rigid models, often fail to provide accurate, reliable, and user-friendly insights. This paper introduces a novel Physics-Informed Neural Network-Based Traffic State Estimator (PINN-TSE), framework that integrates the Aw-Rascle traffic flow model with advanced machine learning and natural language processing (NLP) techniques. By combining physics-informed modeling with data-driven learning, the framework ensures accurate and physically consistent predictions of traffic density and velocity. A multicomponent loss function balances data fidelity with physical constraints, while Large Language Models (LLMs) generate contextualized and interpretable traffic insights through a chat-based web interface. The system is designed to handle diverse user queries from precise spatio-temporal inputs to broad, general inquiries, making it highly adaptable for real-world deployment. Validated on real-world data from the US-101 highway, PINN-TSE demonstrated strong performance in capturing shockwave dynamics and transitions between traffic regimes. It achieved mean absolute errors (MAE) of 2.4 vehicles per mile (vpm) for density and 3.98 mph for velocity, representing improvements of 60% and 73%, respectively, over purely data-driven models. Furthermore, the shockwave speed error was reduced to 8%, significantly improving the reliability of traffic dynamic predictions. The system’s ability to provide actionable insights, such as identifying congestion hotspots and suggesting alternative routes, highlights its practical utility in real-world traffic management. This work makes three key contributions: (1) a robust PINN-TSE framework that embeds physical laws into neural networks, (2) an intuitive LLM-powered interface for real-time traffic interaction, and (3) a demonstration of its effectiveness in real-world settings. By bridging the gap between complex traffic data and human decision-making, this study advances the field of intelligent transportation systems, offering a transformative solution to safer, more efficient, and sustainable traffic management.