解决电商预售运费估算难题，提升预测准确性和可解释性。
RouteCost: A Production-Inspired Multi-Stage Framework for Pre-Order Shipping Cost Estimation in E-Commerce
- 分阶段建模：需求预测+基准定价+残差修正+包裹合并推断
- 在25万订单上实现更高精度与更好的整体校准效果
- 适合关注物流成本预测与系统可解释性的电商研发人员
精准的预售运费估算对电商的价格展示、利润规划和转化率至关重要。实际运费不仅受距离影响，还受目的地需求结构、计费重量、尺寸计价、附加费触发及隐含运营因素（如发货合并）影响。静态查表法忽略关键变化源，而单一回归模型可能捕捉非因果强相关关系。我们提出RouteCost，一个面向生产环境的多阶段框架，将问题分解为：时序感知的需求预测、费用卡驱动的基准定价、第二阶段残差修正，以及基于代理的包裹合并推断。通过路线加权期望聚合，得到产品级运费预测。在超过25万订单、260种商品、18个月的历史数据上，该框架显著提升了预测质量与整体校准性能，同时保持路线级可解释性。
原文摘要 · Abstract (English)
Accurate pre-order shipping cost estimation is important in e-commerce because it affects price presentation, margin planning, and conversion. In practice, shipping cost is shaped not only by distance but also by destination demand mix, billable weight, dimensional pricing, surcharge triggers, and latent operational effects such as shipment consolidation. Static lookup methods therefore miss important sources of variation, while monolithic regressors may exploit strong but non-causal correlations. We propose RouteCost, a production-inspired multi-stage framework that decomposes the problem into time-aware demand forecasting, fee-card-informed baseline pricing, Stage 2 residual correction, and proxy-based box-consolidation inference. Route-level cost estimates are aggregated through a route-weighted expectation formulation to produce product-level shipping cost predictions. Across over 250,000 orders, 260 products, and 18 months of order history, the framework improves predictive quality and aggregate calibration while preserving route-level interpretability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。