Traffic Volume Prediction and Tidal Lane Management System Based on Digital Twin
We introduce a tidal lane management system based on digital twins.The system collects real-time traffic data and utilizes digital twin technology to establish models for predicting traffic volume and managing tidal lanes.By accurately predicting traffic volume,the system achieves dynamic management of tidal lanes,enabling accurate forecasting and effective management of traffic congestion.We conduct an in-depth analysis of factors influencing traffic volume,establish a model for factors affecting traffic volume,and select the extreme random forest model as the best-performing prediction model through multi-model comparisons.Additionally,we introduce various evaluation metrics to assess the extreme random forest model.The results indicate that,across all evaluation metrics,the extreme random forest model achieves the highest accuracy in predicting traffic volume,especially for sudden peak events.Through digital twin technology,we simulate scenarios for tidal lanes and effectively reduce the cost of modifying and validating tidal lane scenarios through digital simulation of different scenarios.This provides theoretical support and data backing for the work of urban traffic management departments.The application of this system can enhance the scientific and efficient management of traffic,broaden the application channels for smart transportation,and provide robust support for addressing urban traffic congestion issues.
digital twintraffic volume predictiontidal lane managementtraffic managementintelligent technology
万方数据
维普
