An AI‐Driven Dynamic Adaptive Management Framework for Road Traffic Safety in Intelligent Transportation Systems

Conventional road traffic safety planning is static and reactive. To address this, an AI‐Driven Dynamic Adaptive Management Framework (AI‐DAMF) for intelligent transportation systems is proposed. The six‐layer architecture integrates multisource data ingestion, knowledge‐guided feature extraction, deep learning‐based pattern recognition, dynamic optimization, IoT‐enabled monitoring, and interactive visualization. Its core innovation is cross‐domain correlation analysis among traffic participants, infrastructure, and operational logistics, which enables proactive risk adjustment and forms a closed‐loop of planning, execution, and evaluation. The primary theoretical contribution is the establishment of a closed‐loop “perception–prediction–optimization–evaluation” paradigm for proactive traffic safety management, shifting the field from reactive incident response to anticipatory risk mitigation. The key practical contribution is a simulation‐validated, scalable framework that transportation engineers and urban planners can directly adopt, achieving a 40% improvement in planning efficiency and a substantial reduction in safety risk identification latency compared to conventional methods. Empirical evaluations further show a decrease in traffic conflict rates under peak‐hour conditions, and the AI‐driven predictor achieves an F1 score of 0.8345 (precision 0.8421 and recall 0.8269). This work delivers a deployable framework that operationalizes the proposed paradigm, ready for regional road traffic safety planning deployment. While the full framework is proposed as an extensible architecture, this study focuses on the implementation and experimental validation of the core risk prediction component.