用地图网格+机器学习解决货车与货单匹配难题，提升定位精度和覆盖范围。
Intelligent Truck Matching in Full Truckload Shipments using Ping2Hex approach
- 将GPS数据转为六边形网格特征，用概率排序匹配货车与货单
- 北美精度提升26个百分点，欧洲14点，覆盖率翻倍
- 适合物流平台、供应链系统研发者参考
基于GPS数据的精准货车与货单匹配是实现整车运输供应链可视化的基础，支持实时追踪和准确到达时间预测。然而，缺失或损坏的车辆标识导致传统匹配方法失效，使部分货单失去可见性。本文提出智能货车匹配系统ITM 2.0，将匹配问题建模为概率排序任务。该方法利用Uber H3六边形空间索引将GPS位置信息离散化为路线相似性特征，结合时间信息，采用LightGBM梯度提升模型，并通过阈值后处理优化结果。经过离线模型对比（SVM、XGBoost、LightGBM）、全面消融实验及生产环境影子测试，验证了其显著优于规则基线。ITM 2.0在北美实现26个百分点的精度提升，在欧洲达14点，同时覆盖率翻倍。该系统已在Project44生产环境中部署，可有效应对高达1公里的地理编码误差、多候选货车及稀疏位置数据等挑战。
原文摘要 · Abstract (English)
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.
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