基于分位数回归模型的物流分拣中心短期货运量预测与风险量化研究

Research on Short Term Freight Volume Prediction and Risk Quantification of Logistics Sorting Centers Based on Quantile Regression Model

摘要: 随着全球贸易深化与消费需求升级，现代物流已成为衔接生产与消费、降低社会成本、保障供应链韧性并支撑经济高效运转的核心基础设施。为解决物流分拣中心货运量存在时间波动性与不确定性，导致资源调配不合理的问题，本文提出分位数随机森林(Q-RF)与分位数LSTM (Q-LSTM)两种模型，用于货运量预测。研究以57个分拣中心过去3个月日货运量为基础，预测未来7天货运量。本研究初步显示，分位数回归方法可对货运量波动风险进行有效量化，并且在短期货物量预测中随机森林的稳定性与准确性表现相对更优，其结果能为物流分拣中心的人力设备调配、作业流程优化提供一定数据支撑，进而可能对降低运营成本、提升物流网络服务效率与客户满意度。

Abstract: With the deepening of global trade and the upgrading of consumer demand, modern logistics has become the core infrastructure that connects production and consumption, reduces social costs, ensures supply chain resilience, and supports efficient economic operation. To solve the problem of time fluctuation and uncertainty in the freight volume of logistics sorting centers, which leads to unreasonable resource allocation, this paper proposes two models, quantile random forest (Q-RF) and quantile LSTM (Q-LSTM), for freight volume prediction. Based on the daily freight volume of 57 sorting centers in the past 3 months, predict the freight volume for the next 7 days. This study preliminarily shows that quantile regression method can effectively quantify the risk of freight volume fluctuations, and the stability and accuracy of random forests are relatively better in short-term cargo volume prediction. The results can provide certain data support for the allocation of human equipment and optimization of operation processes in logistics sorting centers, which may further reduce operating costs, improve logistics network service efficiency and customer satisfaction.