Road Surface Condition Prediction via Regional Weather Cell Modeling
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Solution Overview
Problem
Existing methods for predicting weather-related surface conditions on road segments are inefficient and require a high number of fixed stations or vehicles equipped with sensors, making it difficult to obtain reliable data across an entire road network.
Innovation Solution
A method that partitions a geographical area into weather cells with similar climatic characteristics, trains prediction models for each region, and associates each road segment with multiple models to infer and combine predictions for accurate surface condition estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive map of roadway state is created using fixed stations or contributing vehicles with sensors, then measurement precision of surface conditions is improved, but device complexity and quantity of substance required increase significantly
Solution Approach 1:
The patent creates virtual copies of roadway state information by training prediction models on data from actual sensors. These models generate synthetic surface condition data for road segments without physical sensors, effectively copying the measurement capability from equipped locations to unequipped locations through computational modeling rather than physical duplication of sensor infrastructure
Solution Approach 2:
The patent introduces prediction models as intermediary elements between actual sensor measurements and the needed roadway state information. These models act as mediators that translate weather forecast data and limited sensor observations into comprehensive surface condition predictions, eliminating the need for direct sensor deployment at every location
2Measurement precision
If weather cells are subdivided into regions with similar climatic characteristics and multiple prediction models are trained for each region, then measurement precision is improved, but loss of information increases due to data partitioning
Solution Approach 1:
The patent applies local quality by training distinct prediction models for different climatic regions and road segment types. Each model is specialized for its specific region's characteristics, allowing predictions to be tailored to local conditions rather than applying a single generic model, thereby improving precision while managing information loss through targeted specialization
Solution Approach 2:
The patent segments the geographical area into weather cells and further into regions with similar climatic characteristics. This segmentation allows the system to process and model different climatic zones separately, improving prediction accuracy for each region while the overall system maintains a comprehensive view through the collection of regional models
3Measurement precision
If multiple prediction models are associated with each road segment and predictions are combined, then measurement precision is improved, but loss of time increases due to processing multiple models
Solution Approach 1:
The patent performs preliminary action by pre-training multiple prediction models on historical data and pre-processing weather forecast data into required formats before actual prediction is needed. This advance preparation reduces the computational burden during real-time prediction, allowing multiple models to be executed quickly when surface condition information is actually required
Solution Approach 2:
The patent merges predictions from multiple models by combining their outputs to generate a consolidated surface condition assessment. This combining process integrates the strengths of different models while averaging out individual uncertainties, improving overall prediction reliability through ensemble methodology
Data Source
AI summary
A method for predicting a weather-related surface condition of a particular road segment including partitioning the geographical area into a plurality of weather cells, subdividing the geographical area into regions composed of weather cells sharing similar climatic characteristics, and for each defined region, training at least one prediction model on variables derived from weather observations associated with surface conditions observed in the region in question, and associating each road segment of the network with at least two particular prediction models. The method further including, when a command to predict a surface condition is triggered for a particular road segment, selecting at least the at least two particular models associated with the segment in question, inferring the selected models from meteorological data obtained for the geographic location of the road segment to obtain a plurality of predictions for the segment, and combining the plurality of obtained predictions to obtain a consolidated surface condition for the segment.