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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“,”This paper mainly discusses the problems in large scale traffic volume distribution, which includes the selection of distribution models, the data preconditioning and the valuation of large sparse matrixes for certain kinds of goods, etc. Analysis and computation show that the improved increasing coefficient model and physical analogue model are suitable to the prediction of large scale railway origin destination traffic volume distribution. By using numerical decomposition and superposition of the predicted transportation flux, the planned data can be processed with higher precision. Although from the point of view of computation it is possible to assign tiny values to the zero elements of the origin destination matrixes, it conceals the actual unbalance in local area networks.