Intelligent Truck Matching in Full Truckload Shipments using Ping2Hex approach

TL;DR

This paper introduces ITM 2.0, a machine learning system that improves truck-to-shipment matching accuracy using GPS data and spatial-temporal features, significantly enhancing supply chain visibility.

Contribution

The paper presents a novel probabilistic ranking approach leveraging Uber H3 spatial indexing and LightGBM, achieving substantial accuracy and coverage improvements over rule-based methods.

Findings

26 percentage point precision improvement in North America

14 percentage point precision improvement in Europe

Doubles coverage compared to baseline methods

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 spatial indexing to discretize GPS pings into route similarity features, combined with temporal information, then applies LightGBM gradient boosting with threshold-based post-processing. Through rigorous evaluation including offline model selection (SVM, XGBoost, LightGBM), comprehensive ablation studies, and production shadow testing, we demonstrate substantial gains over rule-based baselines. ITM 2.0 achieves 26 percentage point precision improvement in North America and 14 points in Europe, while doubling coverage. Deployed in production at Project44 handling full truckload shipments, the system demonstrates robustness to geocoding errors up to 1 km, multiple candidate trucks, and sparse pings.