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在电子商务平台中，准确的预购运费估算至关重要，因为它直接影响价格展示、利润规划以及转化率。除了简单的地理距离外，运费还受到多种复杂因素的影响，包括目的地需求组合、计费重量、体积计重、附加费触发条件以及诸如包裹合并等运营效应。传统的静态查找方法无法捕捉这些多样化的变化，而单体机器学习回归器往往依赖于非因果关联。

为了解决这些局限性，作者提出了 RouteCost——一个受生产实践启发的多阶段框架，它将估算过程系统性地分解为：1. 时间感知的需求预测；2. 费率表感知的基准定价；3. 第二阶段残差校正；4. 基于代理的箱体合并推理。通过通过路线加权期望公式聚合路线级成本估算，RouteCost 能够准确地产出产品级的运费预测。该框架在包含超过 25 万个订单、260 种产品以及 18 个月订单历史的大型数据集上进行了评估，显著提升了预测质量和总体校准度，同时保持了强大的路线级可解释性。

RouteCost: A Production-Inspired Multi-Stage Framework for Pre-Order Shipping Cost Estimation in E-Commerce

RouteCost: A Production-Inspired Multi-Stage Framework for Pre-Order Shipping Cost Estimation in E-Commerce

arXiv: arXiv:2607.16230 [cs.LG]
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Authors: Xianling Zeng, Zihan Yu, Sichen Zhao, Yalun Qi, Zhiming Xue
Submission History: Submitted on 24 Jun 2026; last revised 19 Aug 2026.

arXiv: arXiv:2607.16230 [cs.LG]
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Authors: Xianling Zeng, Zihan Yu, Sichen Zhao, Yalun Qi, Zhiming Xue
Submission History: Submitted on 24 Jun 2026; last revised 19 Aug 2026.

📌 Summary

📌 Summary

Accurate pre-order shipping cost estimation is vital for e-commerce platforms as it directly impacts price presentation, margin planning, and conversion rates. Shipping costs are influenced by multiple complex factors beyond mere geographic distance, including destination demand mix, billable weight, dimensional pricing, surcharge triggers, and operational effects like shipment consolidation.

Accurate pre-order shipping cost estimation is vital for e-commerce platforms as it directly impacts price presentation, margin planning, and conversion rates. Shipping costs are influenced by multiple complex factors beyond mere geographic distance, including destination demand mix, billable weight, dimensional pricing, surcharge triggers, and operational effects like shipment consolidation.

Traditional static lookup methods fail to capture these diverse variations, while monolithic machine learning regressors often rely on non-causal correlations. To address these limitations, the authors propose RouteCost—a production-inspired multi-stage framework that systematically decomposes the estimation process into: 1. Time-aware demand forecasting 2. Fee-card-informed baseline pricing 3. Stage 2 residual correction 4. Proxy-based box-consolidation inference

Traditional static lookup methods fail to capture these diverse variations, while monolithic machine learning regressors often rely on non-causal correlations. To address these limitations, the authors propose RouteCost—a production-inspired multi-stage framework that systematically decomposes the estimation process into: 1. Time-aware demand forecasting 2. Fee-card-informed baseline pricing 3. Stage 2 residual correction 4. Proxy-based box-consolidation inference

By aggregating route-level cost estimates through a route-weighted expectation formulation, RouteCost accurately yields product-level shipping cost predictions. Evaluated across an extensive dataset of over 250,000 orders, 260 products, and 18 months of order history, the framework significantly enhances predictive quality and aggregate calibration while maintaining strong route-level interpretability.

By aggregating route-level cost estimates through a route-weighted expectation formulation, RouteCost accurately yields product-level shipping cost predictions. Evaluated across an extensive dataset of over 250,000 orders, 260 products, and 18 months of order history, the framework significantly enhances predictive quality and aggregate calibration while maintaining strong route-level interpretability.