Terminal49 has introduced two advanced capabilities to improve the accuracy of estimated times of arrival (ETAs) for containers moving by rail across the United States and Canada. By leveraging a new machine learning model, the company now provides predicted arrival times for nearly every container as soon as it departs a port. This update significantly expands visibility, increasing tracking coverage from 91% to approximately 99%, ensuring users receive reliable data even when shipping lines or rail carriers fail to provide estimates.
The system utilizes an intelligent source selection model that continuously evaluates multiple data streams, including rail carrier updates, shipping line estimates, and Terminal49’s proprietary predictions. Instead of simply relying on the most recent update, the AI-based model analyzes all available ETAs to promote the one most likely to be accurate. As a container progresses toward its destination and more data becomes available, the model re-evaluates the information in real-time to maintain high data quality.
These technical improvements address the historical challenges of inland rail tracking, where data is often fragmented or unavailable. By segmenting journeys and applying automated validation, Terminal49 has successfully reduced prediction errors by 42%. These tools allow logistics teams to better manage uncertainty and streamline container operations by providing a unified, reliable view of milestones from port departure to final inland arrival.