探讨了大规模铁路运量分布研究中的几个问题：分布模型的选择、数据的预处理及在对某些品类货物预测时稀疏矩阵的赋值等。分析及实际计算表明，改进后的增长系数模型和物理类比模型较适合于大规模铁路区域ＯＤ流分布预测；对预测运量数据进行数值分解和迭加，可以较好地处理计划性数据，从而提高预测精度；预先对大规模稀疏ＯＤ 矩阵零元素赋微量值，从计算的角度看可行，但是也掩盖了局域网络的实际不平衡 Several problems in the study of large-scale railway traffic distribution are discussed: the choice of distribution model, the preprocessing of data and the assignment of sparse matrix in predicting certain types of goods. The analysis and practical calculation show that the improved growth coefficient model and physical analogy model are more suitable for the prediction of OD flow distribution in large-scale railways. The predicted traffic data are numerically decomposed and superposed so that the planning data can be handled well Improve the prediction accuracy. It is feasible from a computational perspective to pre-scale the zero elements of the large-scale sparse OD matrix, but also masks the actual imbalance of the local area network