UTDT: A Baseline Benchmark for Urban Traffic Flow Digital Twins via Roadside LiDAR–Camera Fusion

Digital twin (DT) technology has emerged as a key enabler for bridging the gap between physical and virtual worlds, providing real-time synchronization, monitoring, and optimization capabilities. However, in the field of intelligent transportation and autonomous driving, most existing DT systems remain limited to single-scene simulations or simplified traffic models, lacking the ability to maintain spatiotemporal consistency and continuous vehicle identities across multiple intersections. To address these limitations, this paper proposes an Urban Traffic Flow Digital Twin (UTDT) framework that builds a high-fidelity, multi-scene representation of traffic flow. UTDT integrates four core modules: single-intersection multi-object tracking, multi-intersection trajectory association, trajectory inference and restoration, and twinning synchronization. Multi-object tracking is enhanced by fusing LiDAR and camera data and leveraging re-identification for cross-scene identity continuity, enabling accurate trajectory reconstruction and precise digital twin–based vehicle control in complex traffic scenarios. The experimental results show that UTDT can provide high-fidelity simulation scenarios for the testing and optimization of autonomous driving systems, which promotes the development of intelligent transportation systems and provides important support for the construction of future smart cities. The source code and datasets are available at https://github.com/OpenHUTB/traffic_twin