Freight Visibility Platform Using ETA, HOS, and Direction Scores
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Solution Overview
Problem
Most interested parties lack real-time visibility into the status of goods in transit, with growing freight volumes exacerbating this issue.
Innovation Solution
A freight visibility platform providing real-time location tracking and load-to-vehicle association verification through an API, utilizing historical data and driver hours of service to generate a total matching score for vehicle selection, with security features and data management to ensure accuracy and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time tracking and load verification systems are implemented, then freight visibility is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the freight visibility problem into distinct scoring components: ETA score (timing accuracy), HOS score (driver hours of service compliance), and direction score (route alignment). Each component is calculated independently using specific data sources and algorithms, then aggregated into a total matching score. This segmentation reduces system complexity by breaking down the complex verification process into manageable, modular scoring functions that can be implemented and maintained separately.
2Measurement precision
If multiple scoring factors (ETA, HOS, direction) are computed and aggregated, then matching accuracy is improved, but computational load and data processing time increase
Solution Approach 1:
The system computes three distinct scoring factors (ETA, HOS, direction) that may be more than minimally required for basic tracking, but this excessive computation provides comprehensive verification accuracy. Each score is calculated using dedicated algorithms: ETA comparison against actual arrival times, HOS validation against regulatory limits, and direction analysis using historical location data and compass bearings. The aggregation of these multiple partial scores produces a robust total matching score that significantly improves verification accuracy despite the increased computational load.
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: receiving a network request from a computing device, the network request including a company identifier, an estimated time of arrival (ETA), and a location of interest (LOI); computing a first score based on an actual ETA of at least one vehicle associated with the company identifier; computing a second score based on an hours of service value associated with a driver of the at least one vehicle; computing a third score based on a direction of the at least one vehicle; and aggregating the first, second, and third score to generate a total matching score.