Batch modeling method for traffic simulation interacting with digital twin systems

The rapid evolution of smart transportation systems has elevated the role of digital twins as vital visualization interfaces for integrating highway monitoring, management, and services. This study addresses the persistent challenge of siloed systems in smart highways by proposing a novel batch modeling method for traffic simulation that interoperates seamlessly with digital twins. Leveraging Jingde Expressway's pilot project for national transportation advancement, our approach utilizes high-precision lane coordinates from digital twin systems as feature points for simulation modeling. The method incorporates geographic coordinate system conversion, critical feature point extraction, coordinate point reduction, and vehicle heading angle adjustment. This integration not only enhances the digital twin display with simulation trajectory and road evaluation data but also provides a more realistic visualization platform for simulations. To demonstrate its efficacy, we present a case study of a construction and maintenance event on Jingde Expressway, evaluating the impact of opening the hard shoulder. Results reveal that this strategy reduces lane-changing conflict risks by 66.15% and rear-end collision risks by 93.74% compared to no control measures, thereby significantly improving road safety while maintaining traffic efficiency.