Roadway Condition Estimation Using Quality-Weighted Data Fusion
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Solution Overview
Problem
Current techniques for estimating roadway conditions do not adequately account for the varying quality of data from different sources, leading to inaccurate and inconsistent estimates of actual conditions.
Innovation Solution
A method and apparatus that assign quality weights to various sources of roadway condition data, such as real-time sensors, toll tags, and historical data, to estimate actual conditions by using a hierarchical model that prioritizes data quality and combines data from multiple sources for robust and granular travel time estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sources are combined without quality weighting, then data coverage is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies local quality by assigning different quality weights to different data sources based on their specific characteristics. Each data source (sensors, toll tags, cell phone data, historical data) receives a quality weight reflecting its reliability, allowing the system to treat each source differently rather than uniformly combining all data.
Solution Approach 2:
The patent changes the parameter of data quality by introducing quality weights that modify the contribution of each data source. These weights are calculated based on factors like data recency, source reliability, and temporal variability, transforming the raw data into quality-adjusted estimates.
2Measurement precision
If only high-quality data sources are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the data collection system into multiple independent data sources (sensors, toll tags, cell phone data, historical data), each with its own quality characteristics. This segmentation allows the system to evaluate and weight each source independently, managing complexity through modular organization rather than treating the system as a monolithic unit.
Solution Approach 2:
The patent introduces quality weights as an intermediary mechanism that mediates between multiple data sources and the final estimation. These weights act as a buffer that simplifies the integration process by pre-evaluating source quality, reducing the computational complexity of combining heterogeneous data sources.
3Productivity
If real-time data sources are used, then productivity is improved, but reliability deteriorates due to temporal quality variation
Solution Approach 1:
The patent applies dynamics by making quality weights time-dependent. Rather than static weights, the system continuously adjusts quality weights based on temporal factors such as data recency and temporal variability. This allows the system to adapt to changing data quality conditions while maintaining real-time estimation capability.
Solution Approach 2:
The patent implements feedback by using quality metrics to continuously evaluate and adjust the contribution of each data source. The system monitors data quality over time and adjusts quality weights accordingly, creating a closed-loop system that improves reliability through continuous quality assessment and adaptation.
Data Source
AI summary
Actual conditions of a roadway segment are estimated by providing roadway condition data to a processor for the roadway segment from a plurality of different types of sources of the roadway condition data, assigning a quality to each of the plurality of different types of sources of the roadway condition data, and estimating in the processor the actual conditions of the roadway segment by using the roadway condition data and the quality of each of the plurality of different types of sources of the roadway data. The quality determines weightings given to each of the plurality of different types of sources.