The daily occurrence of traffic accidents has led to the development of 3D reconstruction as a key tool for reconstruction, investigation, and insurance claims. This study proposes a novel virtual-real-fusion simulation framework that integrates traffic accident generation, unmanned aerial vehicle (UAV)-based image collection, and a 3D traffic accident reconstruction pipeline with advanced computer vision techniques and unsupervised 3D point cloud clustering algorithms. Specifically, a micro-traffic simulator and an autonomous driving simulator are co-simulated to generate high-fidelity traffic accidents. Subsequently, a deep learning-based reconstruction method, i.e., 3D Gaussian splatting (3D-GS), is utilized to construct 3D digitized traffic accident scenes from UAV-based image datasets collected in the traffic simulation environment. While visual rendering by 3D-GS struggles under adverse conditions like nighttime or rain, a clustering parameter stochastic optimization model and mixed-integer programming Bayesian optimization (MIPBO) algorithm are proposed to enhance the segmentation of large-scale 3D point clouds. In the numerical experiments, 3D-GS produces high-quality, seamless, and real-time rendered traffic accident scenes achieve a structural similarity index measure of up to 0.90 across different towns. Furthermore, the proposed MIPDBO algorithm exhibits a remarkably fast convergence rate, requiring only 3–5 iterations to identify well-performing parameters and achieve a high \({R}^{2}\) value of 0.8 on a benchmark cluster problem. Finally, the Gaussian Mixture Model assisted by MIPBO accurately separates various traffic elements in the accident scenes, demonstrating higher effectiveness compared to other classical clustering algorithms.