Скачать или смотреть Advancing Traffic Management: Digital Twin Model for Smarter Intersection Design and Optimization

Digital twins, virtual replicas of traffic intersections, offer numerous advantages. They act as reliable reference models, aiding decision-making with real-time data. They enhance understanding of junction behavior, assist in testing new traffic lights configurations. A digital twin of a single intersection is a virtual representation or mirror image of the actual physical intersection in the digital realm. It is created by integrating real-time data from various sources, such as traffic sensors, cameras, and weather monitoring systems, into a unified virtual model. This digital twin serves two primary purposes: traffic prediction and acting as a reference model.
The digital twin of the intersection utilizes advanced machine learning algorithms and time series prediction techniques to forecast future traffic conditions accurately. By analyzing historical traffic patterns and considering real-time data, the digital twin can make proactive predictions of traffic congestion, volume, and flow patterns. This predictive capability allows traffic managers and planners to anticipate and address potential traffic issues, such as congestion hotspots or peak hour bottlenecks, in advance.
The digital twin acts as a reliable reference model for the actual intersection. It continuously updates itself with live data, providing an accurate representation of the real-world traffic conditions in real-time. Traffic engineers and city officials can use this reference model to compare the predicted traffic behavior with the actual observed traffic. By doing so, they can assess the accuracy of the predictions and fine-tune the predictive algorithms if necessary.

Additionally, the digital twin serves as a safe and controlled environment for testing and experimenting with various traffic management strategies. Traffic light timings, lane configurations, and other parameters can be modified and tested within the digital twin, without impacting the actual intersection. This allows traffic engineers to gauge the effectiveness of different interventions and optimize traffic flow management strategies before implementing them in the real world.