Vehicle-Transaction Correlation for Asynchronous Fleet Log Matching
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Solution Overview
Problem
Traditional fleet management strategies rely on manual processes and historical data analysis, which are time-consuming, labor-intensive, and prone to errors, and lack personalized and real-time driver coaching solutions.
Innovation Solution
A comprehensive system that integrates real-time data analytics and machine learning to associate transactions with specific vehicles, provides personalized driver coaching, and optimizes fuel management by identifying alternative service providers using advanced algorithms and visualization tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes and historical data analysis are used for fleet management, then implementation simplicity is maintained, but productivity and time efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preprocessing telematics data, transaction data, and vehicle data in real-time before optimization is needed. Data is normalized, correlated, and stored in structured formats ahead of time, enabling rapid query and analysis when optimization decisions are required, thus improving productivity without proportionally increasing operational complexity
Solution Approach 2:
The patent introduces an intermediary data processing layer that sits between raw data sources and optimization algorithms. This intermediary layer correlates telematics data with transaction and vehicle data, performing preliminary analysis and data reconciliation that simplifies the work of downstream optimization algorithms, thereby improving overall system productivity while managing complexity through modular architecture
2Measurement precision
If real-time data correlation and advanced analytics are implemented, then measurement precision and decision accuracy improve, but device complexity and computational requirements worsen
Solution Approach 1:
The system segments the complex data correlation task into distinct modular components: telematics data processing module, transaction data processing module, vehicle data processing module, and correlation engine. Each module handles specific data types and transformation rules independently, improving measurement precision through specialized processing while managing overall system complexity through clear separation of concerns and reusable components
Solution Approach 2:
The patent applies parameter changes by transforming raw data into standardized formats with consistent units, time zones, and reference frames before correlation. Telematics data, transaction data, and vehicle data are all normalized to common parameters enabling accurate matching. This preprocessing transformation improves association accuracy while the automated nature of the transformations keeps implementation complexity manageable
3Loss of information
If comprehensive data integration from multiple sources is performed, then information completeness improves, but loss of time for data processing worsens
Solution Approach 1:
The system maintains continuity of useful action by implementing continuous real-time data collection and processing pipelines. Telematics devices, transaction systems, and vehicle systems continuously stream data that is immediately processed, correlated, and made available for optimization. This eliminates batch processing delays and ensures information completeness is maintained without significant time loss, as the system operates continuously rather than intermittently
Solution Approach 2:
The system performs preliminary data correlation and validation actions as data arrives from various sources. Rather than waiting to collect all data before processing, the system pre-processes and correlates data streams in real-time, maintaining readiness for optimization queries. This preliminary action ensures comprehensive data integration is achieved without delaying the optimization decision-making process
Data Source
AI summary
In some implementations, the techniques described herein relate to a method including: receiving a data log entry associated with a driver that includes a service provider location and a timestamp; identifying a vehicle associated with the driver based on the data log entry by identifying the vehicle includes applying a machine learning model to the data log entry and a vehicle database; loading a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry includes applying a rule-based optimization algorithm to a historical service provider database; and transmitting a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry.