Core Insights - The rapid increase in vehicle numbers and diverse travel demands have led to heightened road safety risks and illegal activities such as street racing, necessitating a new management model for traffic governance [1][2] - Yihualu, a traffic data service provider, emphasizes a "data-driven decision-making and model-enabled governance" approach, integrating multi-source data and self-developed modeling tools to transition traffic management from reactive to proactive [1][2] Data Integration and Modeling - Traditional traffic management suffers from fragmented and low-quality data, making it difficult to create comprehensive profiles of risk objects. Yihualu addresses this by integrating comprehensive business data and dynamic perception data to form a multi-source data pool covering "people-vehicles-roads-environment" [1] - The "Nighttime Street Racing Vehicle Analysis Model" combines four core data types: vehicle basic data, checkpoint passing data, illegal modification data, and driving license data, processed through a "cleaning-standardization-association" workflow to establish a database for illegal street racing [1] Algorithm and Risk Assessment - The modeling process utilizes advanced algorithms such as entropy weight method and fuzzy evaluation to construct a multi-dimensional risk assessment system. The "Road Hazard Driver and Vehicle Risk Warning Model" extracts risk features and calculates risk scores, categorizing drivers into four risk levels: high, medium, low, and minimal [1][2] - For street racing, the model focuses on temporal and spatial dimensions to accurately identify illegal locations and trajectories, providing a basis for interception and punishment [1] Governance and Implementation - Yihualu has established a closed-loop governance system of "model warning - graded disposal - effect tracking," implementing tiered management for different risk objects. High-risk individuals receive real-time alerts and targeted education, while medium-risk individuals are referred to community management [2] - After three months of model implementation, over 300 high and medium-risk individuals were identified, resulting in more than 20 safety education sessions and the seizure of 330 illegally modified vehicles, contributing to a reduction in accident rates among key groups [2] Future Directions - The model promotes long-term governance mechanisms, including the revocation of driving licenses for unsuitable drivers and collaborative public awareness campaigns, fostering a co-governance framework [2] - Yihualu plans to continue deepening the integration of "data + algorithms + scenarios" to expand application scenarios and support the creation of a safe, efficient, and orderly urban traffic environment [2]