Methods and systems for advanced traffic analysis

Abstract

A system and method for advanced traffic analysis and management are provided, utilizing a network of sensors, including license plate cameras and alternative sources such as radar or inductive loops, to capture granular traffic data. The data is transmitted to a central or distributed server including modules for collection, anonymization (e.g., via hashing or differential privacy), storage, and analysis using machine learning models. External data from third-party sources, such as weather or event feeds, is merged with captured data to form combined datasets for identifying traffic conditions, including volumes, patterns, anomalies, and cross-jurisdictional flows. Outputs include commands to traffic infrastructure (e.g., signal timing adjustments), notifications to user interfaces, responses to inquiries, and exported datasets for further processing. The approach enables real-time predictive insights, privacy-compliant integration, and scalable urban planning while repurposing existing infrastructure for enhanced efficiency and safety.

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