Urban traffic digital twin system development in unity

Abstract

This paper addresses three critical challenges in urban traffic digital twin systems: (1) achieving high-fidelity multi-source data fusion, (2) overcoming computational bottlenecks in large-scale real-time traffic simulation, and (3) enabling intuitive dynamic interaction for enhanced decision-making. To this end, we propose and implement a novel Unity-based digital twin system with a three-layer architecture. This architecture comprises: (1) a System Construction Layer integrating Google Maps, BlenderGIS, and CityEngine via Building Information Modeling (BIM) and Geographic Information Systems (GIS) fusion to generate sub-meter accuracy 3D models (Root Mean Square Error (RMSE) ≤ 0.15m) with parametric Computer Generated Architecture (CGA) road editing; (2) a Data Acquisition Layer synchronizing Amap Application Programming Interface (API) traffic flow and OpenWeatherMap weather data to drive real-time environmental responses (e.g., rain particles = API intensity × 80); and (3) a Concept Generation Layer implementing an optimized 3-Degrees of Freedom (3-DoF) vehicle dynamics model with linear tire stiffness. Key innovations include Graphics Processing Unit (GPU)-accelerated collision detection (Unity Physics), an adaptive Level of Detail (LOD) strategy for 1,500-vehicle concurrent simulation at 60 Frames Per Second (FPS), and closed-loop decision feedback. Validated on an i7-13700H/RTX 4060 platform, the system reduces Central Processing Unit (CPU)/GPU utilization by 47%/48% versus nonlinear models while maintaining trajectory error < 0.23m (9.5%) in 80 km/h emergency scenarios. Comprehensive comparative experiments confirm its efficacy, providing crucial technical support for smart traffic management, autonomous driving testing, and policy pre-evaluation.