arXiv:2605.07733v2 Announce Type: replace Abstract: Accurate truck-to-shipment matching using GPS data is foundational for full truckload supply chain visibility, enabling real-time tracking and accurate estimated time of arrival (ETA) predictions. However, missing or corrupted vehicle identifiers prevent traditional matching approaches, leaving shipments without visibility. This paper presents Intelligent Truck Matching (ITM) 2.0, a machine learning system that addresses this critical gap by formulating matching as a probabilistic ranking problem. Our approach leverages Uber H3 hexagonal spat
The increasing availability of GPS data and advancements in machine learning are enabling more sophisticated solutions for logistics challenges that previously lacked real-time visibility.
Improved truck-to-shipment matching enhances supply chain efficiency, reduces waste, and allows for more accurate planning and resource allocation in the logistics sector.
Traditional matching approaches that struggled with missing identifiers are being augmented or replaced by probabilistic machine learning systems for real-time tracking.
- · Logistics companies
- · Supply chain software providers
- · E-commerce platforms
- · Manufacturers
- · Logistics providers relying on outdated manual tracking
- · Companies with high rates of shipment delays due to poor visibility
More efficient full truckload shipments due to better visibility and matching.
Reduced fuel consumption and operational costs for trucking companies through optimized routes and reduced idle times.
Enhanced overall supply chain resilience and predictability, potentially impacting inventory management and manufacturing schedules globally.
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Read at arXiv cs.LG