Commute Location Clustering for Accurate Vehicle Risk Mapping
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Solution Overview
Problem
Existing systems fail to effectively classify commute locations from user location data, leading to inaccuracies in understanding mobility behavior and estimating risk, which affects risk modeling and insurance pricing.
Innovation Solution
A system that aggregates and clusters commute location data using hierarchical levels, applies temporal filters, and generates a commute location map with risk indexing, utilizing machine learning algorithms to enhance risk modeling and insurance pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hierarchical clustering and machine learning algorithms are used to classify commute location data, then the accuracy of risk modeling and insurance pricing is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing commute location data into multiple hierarchical cluster levels (e.g., primary clusters, secondary clusters, tertiary clusters). This multi-level segmentation allows the system to process and classify location data in manageable stages, improving measurement precision for risk modeling while managing computational complexity through structured organization of data processing tasks.
Solution Approach 2:
The patent implements preliminary action by performing data aggregation and noise filtering before clustering operations. The system pre-processes raw location data to identify and remove noise points, and aggregates location data into meaningful clusters before applying machine learning algorithms. This preliminary processing reduces the complexity of subsequent risk modeling computations while maintaining high accuracy.
2Measurement precision
If noise filtering is applied to commute location data, then the accuracy of mobility pattern representation is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by implementing selective noise filtering that processes only the necessary portion of location data. The system identifies and filters noise points based on specific criteria (e.g., location points that deviate significantly from typical commute patterns) rather than processing all data points uniformly. This approach improves mobility pattern representation accuracy while minimizing the time loss associated with comprehensive filtering.
3Measurement precision
If multiple hierarchical cluster levels are used to classify location data, then the detail and accuracy of commute location identification is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent applies dimensionality change by organizing location data into multiple hierarchical cluster levels that add structural dimensions to the data. Instead of using a single flat clustering approach, the system creates layered clusters (e.g., regional clusters, neighborhood clusters, street-level clusters) that provide detailed location identification while managing complexity through hierarchical organization. This multi-dimensional approach improves identification accuracy without proportionally increasing system complexity.
Data Source
AI summary
Methods, computer-readable media, software, and system may generally identify, determine, and understand the significance of commute location data using telematics data. The system and methods may identify significant commute location data and points by analyzing telematics data and capturing GPS locations associated with the mobility of a user. The commute location data may be classified as data points including origin, destination, and waypoints. This commute location data may be used with metadata to identify significant locations associated with the user. The commute location data may also be used with metadata to understand mobility behavior of the user. Lastly, the commute location data may be used with metadata to determine risk associated with the user, such as based on a risk map.