Fleet Management Interfaces for Real-Time Toll Fee Estimation
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Solution Overview
Problem
Existing fleet management systems face challenges in accurately identifying and managing toll transaction errors, particularly in rental fleets, due to delayed toll fee bills and the difficulty in correlating telematic data with toll transaction data, leading to increased customer and rental car company losses.
Innovation Solution
Systems and methods that generate real-time or near-real-time estimated toll fees by correlating telematic data with toll information databases, allowing for the prediction and aggregation of toll transactions, and integrating this data into rental invoices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If toll agencies generate toll fee bills after significant time has passed since the actual toll transaction, then toll processing simplicity is maintained, but accuracy of toll billing and customer satisfaction deteriorate
Solution Approach 1:
The system performs preliminary actions by generating estimated toll bills in real-time or near-real-time at the point of rental return, before the official toll agency bills arrive weeks later. This allows rental companies to present accurate toll estimates to customers immediately, improving both billing accuracy and customer experience while maintaining simple toll processing through automated telematics-based calculations
2Ease of manufacture
If toll bills are generated weeks after rental vehicle return, then toll agency processing simplicity is maintained, but ability to collect payment from customer deteriorates
Solution Approach 1:
The system performs preliminary payment collection by generating and presenting estimated toll bills to customers at the point of rental return, when the customer is still accessible. This preliminary action allows the rental company to collect payment immediately while the customer is present, rather than attempting collection weeks later when the customer may be out of state or unreachable, thereby significantly improving payment collection reliability
3Measurement precision
If detailed telematic data is correlated with toll transaction data in real-time, then accuracy of toll error identification improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer that automatically correlates telematic data with toll transaction data through standardized processing pipelines. This intermediary infrastructure includes data normalization layers, matching algorithms, and automated reconciliation processes that handle the complexity of real-time data correlation, thereby achieving high toll error identification accuracy without requiring complex manual systems
Solution Approach 2:
The system enables self-service by automating the entire process of correlating telematic data with toll transactions, generating estimated bills, identifying discrepancies, and presenting results to customers without manual intervention. This automation handles data normalization, trip matching, toll calculation, and error detection autonomously, achieving high accuracy while keeping system complexity manageable through standardized automated processes
Data Source
AI summary
Systems and methods are provided for improved rental fleet management. Systems track and transmit telematic data associated with a rental vehicle using a telematic tracker in the rental vehicle. Systems access a toll information database in order to identify toll-triggering telematic data points and predict any toll transactions that the rental vehicle may have incurred during a particular rental period. In response to receiving a system request, systems generate an estimated total toll fee and transmit the estimated total toll fee to a rental server. The estimated total toll fee is presented with the rental fee in the same rental invoice.