Free-Flow Speed Determination Using Weighted Histogram Clustering
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Solution Overview
Problem
Current methods for determining free-flow speed on link segments are inefficient, as they fail to accurately account for non-congestion periods and often rely on incomplete or outdated traffic data, leading to inaccuracies in traffic predictions and planning.
Innovation Solution
The method involves creating a speed-time cluster application histogram data set for a link segment, which identifies speed-time clusters and their durations, determining free-flow speed by analyzing non-congestion periods, and calculating a historically normalized free-flow speed as a weighted average of speeds from these clusters, to provide a representative transit speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine free-flow speed, then the process is simple, but the accuracy of traffic predictions deteriorates
Solution Approach 1:
The patent segments the determination process into distinct components: collecting probe data, identifying speed-time clusters, determining applicable durations, and calculating weighted averages. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent performs preliminary clustering of speed-time data into distinct patterns before final analysis. By pre-organizing the data into clusters with characteristic durations, the system prepares the information in advance for more accurate free-flow speed calculation without adding complexity during real-time processing.
2Reliability
If historical traffic data is not accounted for, then the calculation is faster, but the reliability of transit speed calculations deteriorates
Solution Approach 1:
The system pre-processes historical probe data to identify speed-time clusters and their applicable durations in advance. This preliminary action stores organized historical patterns that can be quickly referenced during free-flow speed determination, maintaining reliability without requiring extensive real-time processing.
Solution Approach 2:
The patent transforms raw historical traffic data into standardized parameters (speed-time clusters with applicable durations) that capture essential traffic patterns. This parameter transformation allows the system to efficiently utilize historical data while maintaining calculation speed.
3Measurement precision
If incomplete traffic data is used, then the data collection process is simpler, but the accuracy of traffic predictions deteriorates
Solution Approach 1:
The patent extracts only the essential information from probe data - specifically the speed-time clusters and their applicable durations - rather than collecting and processing all possible traffic parameters. This extraction approach maintains prediction accuracy by focusing on the most relevant data elements.
Solution Approach 2:
The system applies different processing qualities to different aspects of the data: detailed clustering analysis for speed patterns, and simplified duration tracking for temporal characteristics. This local quality approach optimizes the balance between data collection complexity and prediction accuracy by tailoring processing depth to specific data characteristics.
Data Source
AI summary
A method comprising determining speed-time cluster application histogram data set for a link segment that comprises a plurality of speed-time cluster application histogram data elements, each speed-time cluster application histogram data element identifying a speed-time cluster and an applicable duration of the speed-time cluster for the link segment throughout a histogram duration, for each speed-time cluster application histogram data element, determining a free-flow speed that is representative of a non-congestion speed indicated by the speed-time cluster, determining a historically normalized free-flow speed for the link segment that is a weighted average of the free-flow speed determined for each speed-time cluster application histogram data element weighted by the applicable duration of the speed-time cluster application histogram data element, and identifying a transit speed of the link segment as being the historically normalized free-flow speed is disclosed.