Vehicle Stop Purpose Classification Using ML and GPS Data
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Solution Overview
Problem
Current vehicle fleet management systems lack an efficient method to classify vehicle stops as work-related or non-work-related, leading to resource inefficiencies and increased costs due to the inability to differentiate between purposeful and non-purposeful vehicle activities.
Innovation Solution
A classification platform utilizing machine learning models trained on GPS data and features such as stop-wise, POI, and sequential features to automatically classify vehicle stops as work-related or non-work-related, even without explicit work order information, by clustering location data and matching it with work order information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle fleet management systems are used without machine learning classification, then the system complexity remains low, but the ability to differentiate between work-related and non-work-related stops is insufficient leading to resource inefficiencies
Solution Approach 1:
The patent replaces traditional rule-based or manual classification methods with machine learning models that automatically analyze GPS data, stop duration, location features, and sequential patterns to classify vehicle stops. This substitution of mechanical/classical systems with intelligent algorithms resolves the contradiction by achieving high classification accuracy without requiring complex manual intervention systems.
Solution Approach 2:
The machine learning model performs self-learning from historical GPS data and work order information, automatically improving its classification accuracy over time without requiring external reconfiguration. The system serves itself by continuously refining its understanding of work-related versus non-work-related stops through pattern recognition in the data it processes.
2Measurement precision
If machine learning models are implemented for stop classification, then stop classification accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system pre-processes GPS data during vehicle operations, continuously feeding data into the machine learning model for real-time or near-real-time classification. By performing classification during or immediately after stops rather than in bulk later, the system minimizes processing delays and enables timely decision-making while maintaining high accuracy through the trained model.
Solution Approach 2:
The machine learning model focuses on analyzing only the most relevant features for classification (stop duration, location coordinates, sequential patterns, POI categories) rather than processing every possible data point. This selective feature analysis achieves high classification accuracy while reducing computational overhead and processing time compared to analyzing complete raw datasets.
3Loss of information
If comprehensive GPS tracking and data collection are implemented, then the ability to analyze stop purposes improves, but resource consumption and costs increase
Solution Approach 1:
The system extracts only the essential features from comprehensive GPS data that are most predictive of stop purpose, such as stop duration, location coordinates, sequential stop patterns, and proximity to points of interest. By extracting and analyzing only these critical features rather than processing complete raw GPS trajectories, the system achieves accurate classification while minimizing data storage and processing resource consumption.
Solution Approach 2:
The analysis focuses on local characteristics of each stop event (duration, immediate location, surrounding POIs) rather than requiring analysis of entire fleet operations or historical patterns. This localized approach to data analysis provides sufficient information for accurate classification without the resource overhead of comprehensive global analysis.
Data Source
AI summary
A device receives location information and work order information associated with multiple vehicles, and groups the location information into engine off information, idling information, and journey information. The device combines, based on the journey information, the engine off information and the idling information to generate vehicle stop information associated with the plurality of vehicles, and matches corresponding work order information with the vehicle stop information to generate matched information. The device extracts stop-wise features, points of interest features, stop cluster features, and sequential features from the vehicle stop information, and utilizes the stop-wise features, the points of interest features, the stop cluster features, and the sequential features with a model to determine work order stops and non-work order stops for the multiple vehicles. The device provides information associated with the work order stops and the non-work order stops for the multiple vehicles.