Vehicle Gateway Cohort Benchmarking With ML Fleet Segmentation
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Solution Overview
Problem
Current fleet management systems rely on intuitive methods for selecting cohorts for comparison, which can lead to biased and misleading comparisons due to confounding attributes, and lack efficient data processing and visualization tools for meaningful decision-making.
Innovation Solution
A vehicle gateway device collects and transmits operational data to a management server, which uses machine learning techniques to determine segmentation attributes and cluster fleets into cohorts based on metrics like distance driven, vehicle type, and geography, presenting interactive graphical user interfaces for real-time data analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If intuitive methods are used for selecting cohorts for comparison, then the selection process is simple and quick, but the comparisons become biased and misleading due to confounding attributes
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary between the raw fleet data and the cohort selection process. The ML model processes segmentation attributes and objectively determines cohort assignments, eliminating human bias while maintaining operational simplicity through automated interfaces.
Solution Approach 2:
The system transforms the cohort selection process from intuitive subjective judgment to objective parameter-based classification. By using machine learning to analyze segmentation attributes (distance driven, vehicle type, geography), the system changes the selection criteria from qualitative intuition to quantitative parameter analysis, improving measurement precision.
2Measurement precision
If machine learning techniques are used to determine segmentation attributes and cluster fleets, then unbiased cohort selection and accurate data processing are achieved, but the system complexity increases
Solution Approach 1:
The patent segments the complex machine learning system into distinct functional modules: data collection from gateway devices, segmentation attribute determination, ML-based cohort clustering, and visualization. This modular segmentation manages system complexity by organizing functions into separate, manageable components.
Solution Approach 2:
The machine learning model operates autonomously to determine segmentation attributes and assign cohorts without requiring manual intervention. The system self-services the complex analytical tasks, reducing the operational burden on users while maintaining high measurement precision.
3Measurement precision
If comprehensive vehicle metric data is collected and analyzed in real-time, then accurate fleet performance measurements are obtained, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining segmentation attributes (distance driven, trip length, vehicle type composition, geography) and pre-training machine learning models on historical data. This preliminary preparation enables faster real-time cohort assignment and performance measurement without sacrificing accuracy.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with machine learning-based computational systems. The ML models efficiently process comprehensive vehicle metric data in real-time, substituting slower conventional analysis methods and reducing data processing time while maintaining measurement precision.
4Ease of operation
If interactive graphical user interfaces are implemented for real-time data visualization, then decision-making is improved, but the device complexity and development costs increase
Solution Approach 1:
The patent implements web-based graphical user interfaces that can be deployed as lightweight, accessible applications without requiring complex native software installations. This approach provides rich visualization capabilities while keeping development and maintenance costs lower than traditional desktop applications.
Solution Approach 2:
The graphical user interface is designed to be universal, providing real-time data visualization, cohort comparison, and decision-making support across multiple platforms and devices. This multi-functional interface consolidates various operational needs into a single system, managing complexity through consolidation rather than proliferation of separate tools.
Data Source
AI summary
A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and/or location data. The system receives data from multiple vehicles and multiple fleets. The system uses machine learning to identify cohorts for fleets. The system calculates metrics for fleets and benchmarks for the cohorts. The system presents the metrics and benchmarks in a graphical user interface.