Vehicle Speed Prediction Using Historical Traffic Similarity

The patent applies preliminary action by predicting future road conditions before they actually occur. The system uses historical traffic data, weather information, and event data to forecast speeds and congestion levels for upcoming time periods, enabling users to plan routes in advance rather than merely reacting to current conditions. This predictive capability allows proactive route selection before congestion develops.

The patent implements dynamics by continuously updating predictions with real-time data and adapting to changing conditions. The system dynamically adjusts speed predictions based on current traffic flow, weather changes, and emerging events, creating a living model that evolves with actual road conditions rather than relying on static historical patterns alone.

The patent applies segmentation by dividing the prediction system into distinct functional modules: historical data processing, real-time data collection, weather impact analysis, event detection, and prediction synthesis. Each module handles specific aspects of the prediction task, allowing the complex system to be managed through modular components that can be independently optimized and maintained.

The patent implements universality by creating a multi-functional prediction system that simultaneously processes multiple data types (traffic flow, weather, events), serves multiple road segments, and provides predictions for multiple future time periods. The unified platform handles diverse prediction tasks using a common architectural framework, reducing overall system complexity despite the multifaceted nature of the predictions.

The patent applies periodic action by implementing scheduled prediction intervals and batch processing of data. Instead of continuously analyzing every incoming data point, the system collects data over defined periods, processes information at regular intervals, and generates predictions at optimized frequencies. This periodic approach balances the need for current information with the time required for accurate analysis.

The patent uses preliminary action by pre-processing and storing historical data in optimized formats before prediction is needed. Historical traffic patterns, weather correlations, and event data are pre-analyzed and organized into structures that enable rapid querying during prediction operations, reducing the time required for actual prediction computations.