Recommended Speed Service for Dynamic Road Conditions

The system performs preliminary actions by collecting and storing speed data in advance during normal operations, preprocessing it through machine learning models to extract recommended speed values. This preparation work is done before statistical analysis is needed, so when a road segment needs speed recommendations, the pre-processed data is already available, eliminating the need to wait for numerous vehicles to pass and reducing the time delay while maintaining statistical reliability.

The patent replaces the mechanical accumulation approach (waiting for multiple vehicles to physically pass and collect data) with an information-based system. Machine learning models process individual vehicle speed data to generate recommended speed values, and these recommendations are shared across the network. This substitution allows the system to provide statistically accurate speed recommendations even with limited data, enabling rapid response to changing conditions while maintaining measurement precision.

The system implements a universal machine learning model that processes speed data from multiple vehicles across diverse road conditions and scenarios. Instead of creating specific solutions for each scene type (rain, snow, road work, etc.), the model learns patterns from aggregated data and generates appropriate speed recommendations for any condition. This multi-functional approach maintains high accuracy across various scenarios while simplifying the system architecture and making it easily scalable to new conditions without additional complexity.