Geographic Location Classification via Vehicle Trip Logs
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Solution Overview
Problem
Existing systems lack effective methods to classify geographic locations based on vehicle trip logs, limiting personalized service targeting and driver profiling.
Innovation Solution
An algorithm that analyzes trip stop times and geo-locations to classify stops as 'Home,' 'Work,' or 'Point of Interest' by calculating time differences and filtering outliers, using these classifications to provide targeted services and driver profiling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle trip logs are analyzed to classify geographic locations, then driver profiling and service targeting accuracy is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the classification process into distinct phases: data collection from trip logs, stop identification based on temporal patterns, location classification using multiple criteria (time of day, duration, frequency), and driver profiling. This segmentation allows each phase to be optimized independently, managing complexity while maintaining high classification accuracy through systematic processing of vehicle telematics data.
2Reliability
If trip stop times are calculated and analyzed for each vehicle, then behavioral pattern recognition is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing trip stop times as vehicles traverse routes, organizing this temporal data in advance for efficient later analysis. Stop patterns are identified by comparing consecutive trip times, and behavioral profiles are built progressively as more data is collected, reducing real-time processing requirements while maintaining high reliability in pattern recognition.
3Adaptability or versatility
If geographic locations are classified into categories such as Home, Work, and Point of Interest, then personalized service delivery is improved, but data storage and management requirements increase
Solution Approach 1:
The patent applies local quality by classifying geographic locations with specific attributes tailored to each location type (Home, Work, Point of Interest). Each classified location stores only the relevant characteristics needed for that classification category, such as time of day patterns for Home/Work locations or visit frequency for Points of Interest. This selective data storage provides personalized service capabilities while optimizing storage efficiency by avoiding redundant information.
Data Source
AI summary
A system and method for the classification of geographic locations based on vehicle trip logs, including: grouping a plurality of trip records by vehicle, wherein the plurality of trip records include start times and locations and stop times and locations; sorting the plurality of trip records for a vehicle in one of descending or ascending order with respect to stop times; calculating a plurality of time differences between consecutive trips of the plurality of trip records; storing the plurality of time differences in a stop time array; for a first plurality of time differences, storing the associated stop locations in a first location array; for a second plurality of stop times, storing the associated stop locations in a second location array; and computing a time difference median for each of the first location array and the second location array and returning the time difference medians as results for each location.