1) railway accommodation
铁路客货运容量
2) railway passenger traffic volume
铁路客运量
1.
To improve the prediction abilities of the present methods for railway passenger traffic volume,support vector regression(SVR)was improved by weighting penalty coefficients using Mahalanobis distance between training and testing samples,and a model for predicting the time serial of railway passenger traffic volume was set up based on the improved SVR.
为了提高铁路客运量现有预测方法的预测能力,用训练样本与测试样本间的马氏距离对惩罚因子进行加权,对传统的支持向量回归机(SVR)进行了改进,在此基础上提出了基于改进SVR的铁路客运量时间序列预测方法。
2.
The share of railway passenger transport market shows continuous decrease status,which caused by long-period lack of transport capacity and down railway passenger traffic volume.
我国铁路长期以来运能短缺,铁路客运量增长受到制约,导致铁路客运市场份额呈现持续下降的态势。
4) railway accommodation
铁路客货运设施
5) railway freight volume
铁路货运量
1.
Railway Freight Volume Prediction Based on Variable-weight Combined Model
基于变权重组合模型的铁路货运量预测
2.
The relation between railway freight volume and its influence factors is complex and nonlinear.
铁路货运量与其影响因素之间存在着复杂的非线性关系,传统的BP神经网络模型能对非线性系统进行很好的拟合,但模型的预测能力不强。
3.
The improved Gray-Markov Method is employed to forecast the future development of the china railway freight volume.
本文将灰色模型预测方法GM(1,1)和马尔可夫链预测相结合,提出灰色马尔可夫链改进预测方法,并且针对我国铁路货运量的发展趋势进行了预测,得出比灰色预测更加准确的结论。
6) highway transportation
公路客货运量
补充资料：客货船
见客船。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条