AI Toll Validation Using Vehicle Type and Axle Synchronization
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Solution Overview
Problem
Traditional toll equipment audits are labor-intensive, costly, and prone to redundancy due to manual data review and reliance on vendor-provided data, leading to inefficiencies and potential misrepresentation of audit results.
Innovation Solution
A computer-based system utilizing AI and machine learning models for intelligent toll validation, which analyzes video data to accurately determine vehicle types and axle counts, synchronizes transaction records, and validates toll transactions autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of toll transaction data is used for audits, then audit independence can be maintained, but audit time consumption increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated computer-based system that uses machine learning models to validate toll transactions. The system automatically compares video data with transaction records, eliminating the need for manual data review while maintaining audit independence through automated verification mechanisms.
Solution Approach 2:
The system performs self-validation by automatically comparing video data against transaction records and generating audit reports without requiring manual intervention. The machine learning model independently analyzes data, identifies discrepancies, and validates toll transactions, enabling the system to serve itself in the audit process.
2Measurement precision
If manual comparison of field data with transaction data is used, then data accuracy can be verified, but labor requirements increase and cost increases
Solution Approach 1:
The patent replaces manual data comparison processes with automated computer vision and machine learning systems. The system automatically extracts vehicle information from video data, compares it with transaction records, and verifies accuracy without requiring manual labor, thereby reducing labor requirements while maintaining or improving data accuracy.
Solution Approach 2:
The system creates digital copies of video data and transaction records for automated comparison. By working with digital replicas of the data rather than physical documents, the system enables automated verification processes that reduce labor requirements while maintaining measurement precision.
3Ease of operation
If reliance on vendor-provided data is used for audits, then audit process simplicity is maintained, but audit reliability decreases due to potential misrepresentation
Solution Approach 1:
The patent introduces an independent computer-based system as an intermediary between vendor data and audit conclusions. This intermediary system automatically validates transaction records against video data, providing an independent verification mechanism that eliminates reliance on vendor-provided data while maintaining operational simplicity through automated processes.
Solution Approach 2:
The system incorporates feedback mechanisms where automatically validated data is compared against original vendor data, and any discrepancies are identified and reported. This feedback loop enables the system to detect and report potential misrepresentations in vendor data while maintaining simple automated operation.
4Device complexity
If traditional audit sampling methods are used, then audit cost is reduced, but measurement precision decreases due to limited data coverage
Solution Approach 1:
The patent implements continuous automated validation of all toll transactions rather than intermittent sampling. The system continuously processes video data and transaction records, providing comprehensive coverage of all transactions while maintaining cost-effectiveness through automated processing that eliminates the need for expensive manual review of large datasets.
Data Source
AI summary
Embodiments provide intelligent toll validation. One such embodiment, using a computer vision model, based on video data associated with a vehicle, determines a first type instance of the vehicle and a first axle count of the vehicle. A toll transaction record associated with the vehicle is identified. The toll transaction record includes a second type instance of the vehicle and a second axle count of the vehicle. A synchronization status is determined based on the determined first type instance of the vehicle, the second type instance of the vehicle, the determined first axle count of the vehicle, and the second axle count of the vehicle. Responsive to the determined synchronization status being positive, the identified toll transaction record is validated.