Road Condition Alert System Using FASST Analysis
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Solution Overview
Problem
Current systems fail to accurately predict hazardous road conditions due to the mismatch between localized weather forecasts and actual road conditions, leading to inadequate alerts for drivers.
Innovation Solution
A method and system that determine specific sections of roads to analyze, correlate road data with localized weather forecasts, and perform surface condition analysis using models like Fast All-Season Soil STate (FASST) to generate alerts for hazardous conditions, which can be disseminated through various channels including mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If localized weather forecasts are used to predict road conditions, then the timeliness of alerts is improved, but the accuracy of predictions deteriorates due to mismatch between weather data and actual road conditions
Solution Approach 1:
The patent introduces road surface temperature sensors and weather data processing systems as intermediaries between raw weather forecasts and road condition predictions. These intermediaries transform generic weather data into road-specific condition assessments by measuring actual road surface temperature and comparing it with ambient air temperature, thereby resolving the mismatch between weather forecasts and actual road conditions while maintaining timely alerts
Solution Approach 2:
The system changes the parameter being measured from ambient air temperature to road surface temperature. By focusing on the specific parameter of road surface temperature rather than general weather conditions, the system achieves more accurate predictions of hazardous road conditions while maintaining the timeliness of alerts through automated monitoring
2Measurement precision
If comprehensive road surface analysis is performed for all road sections, then the accuracy of hazard detection is improved, but the computational complexity and resources required increase
Solution Approach 1:
The patent applies local quality by focusing road surface temperature monitoring on specific high-risk road sections identified through criteria such as historical accident data, geographic features, and traffic patterns. Rather than uniformly monitoring all roads, the system concentrates resources on locations most susceptible to hazardous conditions, thereby maintaining high detection accuracy while reducing overall system complexity
Solution Approach 2:
The system segments the road network into distinct monitoring zones based on hazard risk levels. High-risk sections receive comprehensive sensor monitoring and frequent analysis, while lower-risk sections use reduced monitoring protocols. This segmentation allows the system to achieve high accuracy where needed while managing computational resources efficiently across the entire road network
Data Source
AI summary
Embodiments of the invention are directed to methods and systems for forecasting hazardous road conditions. The method includes determining a plurality of sections of road to analyze and correlating the sections of roads to localized weather forecasts. The method also includes performing a road surface condition analysis for each section of road of the plurality of sections of road and based on a prediction of a hazardous road condition, generating an alert regarding the hazardous road condition.