Telematics Data Compression with Virtual Lines and Intersection Ordinals
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Solution Overview
Problem
Conventional telematics data analysis methods are limited by the large volume of data generated, which restricts their ability to consider multiple roads and intersections simultaneously, and requires significant storage capacity, making them inefficient for comprehensive analysis.
Innovation Solution
The system compresses telematics data using GPS and sensor network information, representing geospatial data with fewer data points by identifying virtual lines and assigning ordinals to intersections, allowing for efficient retrieval and analysis of entire city networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional telematics data collection methods are used to capture detailed geospatial data, then measurement precision is improved, but the quantity of data increases significantly requiring large storage capacity
Solution Approach 1:
The patent transforms continuous geospatial coordinates into discrete ordinal values representing virtual lines and intersections. This parameter transformation reduces data from continuous floating-point coordinates to discrete categorical values, significantly compressing data volume while preserving essential spatial information for analysis.
Solution Approach 2:
The patent creates a simplified abstract representation (copy) of the physical road network using virtual lines and ordinal identifiers. Instead of storing actual GPS coordinates, the system stores references to this abstract model, maintaining analytical utility while reducing storage requirements.
2Adaptability or versatility
If large volumes of telematics data are stored for comprehensive analysis, then analysis completeness is improved, but device complexity and storage requirements increase
Solution Approach 1:
The patent applies parameter transformation to convert complex geospatial data into simplified ordinal representations. This enables comprehensive city-wide analysis by reducing the computational and storage complexity associated with handling detailed coordinate data for all roads and intersections.
Solution Approach 2:
The patent segments the continuous road network into discrete virtual lines and intersections, each assigned ordinal identifiers. This segmentation allows the system to handle comprehensive spatial analysis by breaking down the complex continuous space into manageable discrete units.
3Measurement precision
If detailed geospatial data is collected for every location point, then measurement precision is improved, but data retrieval efficiency decreases due to large data volume
Solution Approach 1:
The patent transforms detailed coordinate data into compact ordinal representations that reference pre-defined virtual lines and intersections. This parameter transformation maintains location precision for analytical purposes while enabling much faster data retrieval operations by working with compact categorical data instead of large coordinate datasets.
Data Source
AI summary
Example methods, apparatus, and articles of manufacture to compress telematics data are disclosed herein. An example computer-implemented method includes identifying, using one or more processors, a portion of recorded telematics data representing a physical transversal of a physical intersection of two or more road segments, wherein each road segment has an assigned unique ordinal value; identifying, using one or more processors, a first road segment on which the physical transversal entered the intersection; identifying, using one or more processors, a second road segment on which the physical transversal exited the intersection; identifying, using one or more processors, a pair of ordinal values including a first ordinal value assigned to the first road segment, and a second ordinal value assigned to the second road segment; and storing the pair of ordinal values instead of the portion of the recorded telematics data in a compressed representation of the recorded telematics data.