基于节点分类的区域货流分布预测方法

为了更加精确地预测区域货流分布并准确描绘区域经济之间的联系与互动,提出了基于聚类算法与改进重力模型相结合的区域货流分布预测方法。首先,通过Pearson相关性检验对货运影响因素进行分析,针对不同区域之间的异质性和相似性,采用K-means++算法进行节点聚类,对起讫点（origin-destination, OD）对进行精细化研究;其次,引入货运影响因素、社会联系强度以及由距离成本和时间成本构建的阻抗函数作为参数,对传统重力模型进行改进,灵活地适应不同地区的交通特征,使得模型更具通用性和适应性;最后,以云南省为例,利用构建的改进重力模型进行货流分布预测,并与传统重力模型预测结果进行比较。结果表明：改进重力模型预测精度比传统重力模型提升了57.75%,稳定性提升了54.66%。该方法预测精度明显提升,为区域货流预测提供了更为可靠的方法。

A node classification based method for predicting regional cargo flow distribution

To predict regional freight flow distribution more accurately and depict the connections and interactions between regional economies, a method combining a clustering algorithm and an improved gravity model is proposed. First, Pearson correlation analysis is performed on the influencing factors of freight. The K-means++ algorithm is then used for node clustering, considering the heterogeneity and similarity between different regions, to study origin-destination (OD) pairs in detail. Next, the traditional gravity model is improved by introducing parameters such as freight influencing factors, social connection strength, and an impedance function constructed from distance and time costs. This makes the model more adaptable to different regional traffic characteristics, enhancing its generality and applicability. Finally, the improved gravity model is used to predict freight flow distribution in Yunnan Province, and the results are compared with those of the traditional gravity model. The results show that the improved gravity model improves prediction accuracy by 57.75% and stability by 54.66% compared to the traditional gravity model. This method significantly enhances prediction accuracy, providing a more reliable approach for regional freight flow forecasting.