Intersection-Based Telematics Compression for City-Scale Road Analysis
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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 it 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 all roads and intersections in a city-scale context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional telematics data collection methods are used, then comprehensive vehicle monitoring data is obtained, but the data volume becomes excessively large requiring significant storage capacity
Solution Approach 1:
The patent extracts only the essential geometric features (intersection points, road segments, traversal directions) from the complete telematics data stream. By taking out only the critical spatial information needed for analysis while discarding redundant details, the system achieves comprehensive monitoring capability with significantly reduced data storage requirements.
Solution Approach 2:
Instead of collecting all telematics data and then filtering it, the patent inverts the approach by directly capturing only the essential geometric traversal information from the outset. This inversion allows the system to achieve the same analytical purpose with minimal data collection, resolving the contradiction between comprehensive monitoring and data volume.
2Loss of information
If all telematics data is stored for analysis, then complete information is available, but retrieval and analysis of multiple roads and intersections becomes inefficient
Solution Approach 1:
The patent segments the continuous telematics data stream into discrete geometric events (intersection traversals, road segment transitions). By organizing data into segmented, structured records with clear spatial and temporal markers, the system maintains complete information while enabling efficient indexing, querying, and analysis of multiple roads and intersections simultaneously.
Solution Approach 2:
The patent transforms traditional time-series telematics data into a spatial-dimensional representation using geographic coordinates, road network topology, and geometric relationships. This dimensional transformation allows analysts to query and analyze data across multiple roads and intersections simultaneously, dramatically improving analysis productivity while preserving information completeness.
3Measurement precision
If detailed geospatial data is collected for every vehicle movement, then precise location tracking is achieved, but storage requirements increase significantly
Solution Approach 1:
The patent applies local quality by recording detailed geospatial information only at critical locations (intersections, road segments) rather than continuously throughout all vehicle movements. This selective precision maintains accurate location tracking where it matters most for traffic analysis while significantly reducing overall storage requirements by using coarser or no data for intermediate positions.
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.