Road Condition Generation Using Hidden Markov Model Transition Data
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Solution Overview
Problem
Conventional road condition generation methods are prone to errors due to abnormal traffic speeds, leading to inaccurate calculations and limited road coverage, particularly on non-highway roads, as they rely solely on traffic speed data without considering historical transition information and traffic capacity correlations.
Innovation Solution
A method that generates road condition state information by using transition information between road condition states extracted from historical data and correspondence with traffic capacity information, employing a hidden Markov model to smooth out anomalies and provide accurate road condition assessments across various road types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional road condition generation methods rely solely on traffic speed data, then the calculation process is simple, but the accuracy of road condition assessment deteriorates due to abnormal traffic speeds and limited road coverage
Solution Approach 1:
The system performs preliminary extraction of transition information between road condition states from historical road condition data before generating current road condition assessments. This pre-processing of historical data enables the model to learn normal transition patterns, which are then used to filter out abnormal traffic speed data during real-time assessment, thereby improving accuracy without proportionally increasing real-time calculation complexity
Solution Approach 2:
The patent introduces transition information between road condition states as an intermediary element that mediates between raw traffic speed data and final road condition assessments. This intermediary layer, derived from historical data, helps filter out anomalies and provides a more robust basis for assessment, reducing the direct impact of abnormal traffic speeds on accuracy
2Productivity
If the system uses only current traffic speed data for road condition generation, then the data processing is fast, but the road coverage is limited particularly on non-highway roads
Solution Approach 1:
The system pre-extracts transition information from historical road condition data covering various road types including non-highway roads. This preliminary action builds a comprehensive knowledge base that extends road coverage beyond what current traffic speed data alone could provide, while maintaining fast real-time processing by using the pre-built model for current assessments
Solution Approach 2:
The transition information model serves multiple functions: it filters abnormal traffic speeds, provides road condition context for different road types, and enables assessment on non-highway roads where traffic speed data alone may be insufficient. This multi-functionality extends road coverage without significantly increasing processing complexity
3Measurement precision
If the system processes historical road condition data to extract transition information, then the road condition accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs the computationally intensive extraction of transition information from historical data as a preliminary action, separate from real-time road condition generation. This allows the heavy processing to occur offline, while real-time assessments use the pre-built model efficiently, minimizing the time loss during actual road condition generation
Solution Approach 2:
The system extracts only the essential transition information between road condition states from historical data, rather than processing all possible features. This partial action approach focuses computational resources on the most critical patterns needed for accurate assessment, reducing overall processing time while maintaining accuracy
Data Source
AI summary
A road condition generation method is provided for a computing device. The method includes obtaining current driving state information; based on the current driving state information, generating road condition state information according to transition information between road condition states extracted from historical road condition data and a correspondence between the road condition states and road section traffic capacity information; and outputting the road condition state information.