Physics-Informed Neural Network Model for Predictive Risk Assessment and Safety Analysis

This paper presents a physics-informed neural network (PINN) designed to predict the future locations of both vehicles and pedestrians, providing critical insights into road safety risks. By forecasting potential trajectories of road users, the proposed model informs preemptive strategies to avoid accidents. The physics model incorporates the intelligent driver model for vehicles and the social force model for pedestrians. The stochastic nature of risk evaluation is addressed by probabilistically predicting future locations based on the expected distribution in a two-dimensional open space. The framework accurately assesses the risk by predicting the future locations of vehicles and pedestrians within a 2- to 4-s time frame with approximately 2% error rates. The risk evaluation performance of the proposed framework was tested by calculating the time to collision (TTC) between vehicles and pedestrians and analyzing traffic conflicts. Leveraging the probabilistic predictions, the TTC was evaluated stochastically using Monte Carlo simulations and the Kolmogorov–Smirnov test, enabling a more granular and effective traffic conflict analysis. The developed method demonstrated over 95% accuracy when evaluating potentially dangerous events occurring within 3 s or less, providing actionable insights for improving road safety. The framework was deployed in a real-world setting, demonstrating reliable and robust test results. This comprehensive approach is expected to pave the way for more effective risk evaluation and mitigation at intersections and on roads.