Speed Profile Dictionary for Traffic Data Storage Reduction
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Solution Overview
Problem
The existing historic traffic databases face challenges in reducing storage requirements due to the large volume of data, which affects the efficiency and consistency of the data storage and usage.
Innovation Solution
A speed profile dictionary is created using statistical analysis, specifically clustering, to define distinct speed profiles that are matched to location codes on a road network, reducing storage needs and improving data quality by identifying erroneous inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If historic traffic data is stored in traditional databases, then data completeness is maintained, but storage requirements become excessively large
Solution Approach 1:
The patent combines multiple similar speed records into a single speed profile dictionary entry by identifying patterns and similarities across different time periods and locations. This merging process reduces the total number of stored records while preserving the essential traffic information through representative profiles that can be reused across multiple contexts.
Solution Approach 2:
The patent transforms detailed speed measurements into aggregated speed profiles by changing the parameters from individual speed values to statistical representations (minimum, maximum, average speeds). This parameter transformation reduces data volume while maintaining the ability to represent traffic conditions accurately for routing and prediction applications.
2Measurement precision
If detailed historic traffic data is stored, then data accuracy is improved, but storage efficiency deteriorates
Solution Approach 1:
The patent creates simplified copies of detailed speed data in the form of speed profiles that can be reused multiple times. Instead of storing identical or similar speed records repeatedly, the system creates a single representative profile that serves as a template for multiple time periods and locations, maintaining accuracy while improving storage efficiency.
Solution Approach 2:
The patent segments detailed speed data into distinct speed profiles organized by location codes and time periods. This segmentation allows the system to store only the essential characteristics of traffic patterns at each segment, rather than every individual measurement, thereby improving storage efficiency while preserving the ability to retrieve accurate information when needed.
3Reliability
If comprehensive traffic data is maintained, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary processing of speed data by creating speed profiles in advance and organizing them in a dictionary structure. This preliminary action prepares the data for future prediction tasks by pre-computing and storing the essential patterns, reducing the complexity of real-time data processing while maintaining prediction accuracy.
Solution Approach 2:
The speed profile dictionary serves multiple functions: it stores historical data, enables route prediction, supports traffic analysis, and provides a standardized format for data exchange. This universal structure reduces processing complexity by providing a single, efficient data organization method that handles multiple tasks, rather than requiring separate systems for each function.
Data Source
AI summary
A speed profile dictionary and associated lookup tables are disclosed. A set of distinct speed profiles is defined using a statistical analysis routine. Preferably, the statistical analysis routine uses clustering. The speed profiles are then matched to location codes identifying physical locations on a road network and days of the week. Applications using historic traffic data may use the speed profile dictionary and one or more lookup tables instead of a complete historic traffic database, thereby reducing the amount of memory needed to store historic traffic data.