1) highway transportation
公路客货运量
2) Highway Passenger Carrying Capacity
公路客运量
1.
Forecast for Highway Passenger Carrying Capacity Based on Multiple ARIMA Model;
基于乘法ARIMA模型的公路客运量预测
3) highway freight volume
公路货运量
1.
In the paper,aiming at shortcoming of tradition Gray prediction model,the fluctuation polynomial was utilized to displace the exponent curve in the model of GM(1,1),and the prediction results were adjusted by Markov chain,then the prediction model based on improved Gray-Markov chain was established,and the highway freight volumes were predicted by the model.
针对灰色模型的预测结果精确度受原始数据变化幅度的影响较大,且要求累加生成的数据列具有指数性质的缺点,采用带波动的多项式来替代GM(1,1)模型中的指数形曲线,并通过马尔可夫链对其预测结果进行修正,从而建立改进的灰色-马尔可夫链预测模型,同时利用该改进模型对我国公路货运量进行预测,经分析表明预测结果具有较高的精度,预测方法具有一定的可行性和有效性,预测结果可指导公路建设与管理。
2.
The combination forecasting model can serve as the newest idea for predicting the highway freight volume of Zhejiang province.
在合理选择单一预测模型的基础上,通过求解近似最优非负权重来建立组合预测模型,并运用概率统计方法对模型的适用性进行了验证,为浙江省公路货运量的预测提供了新思路。
4) railway accommodation
铁路客货运容量
5) quality of highway passenger transportation
公路客运质量
1.
This model can avoid subjectivity to some extend during the evaluating process and give effective evaluation of the quality of highway passenger transportation.
通过AHP法对公路客运质量指标体系进行分析 ,并使用BP人工神经网络 ,建立对公路客运质量评价的模型 ,该模型可在一定程度上避免人在评价过程中的主观性 ,能够有效地对公路客运质量进行评价 ,同时该模型具有学习能力 ,对公路客运质量管理与决策活动有重要的支持作用。
6) Road freight turnover
公路货运周转量
1.
The simulation of the algorithm took the road freight turnover as an example,used BP algorithm and the extended Kalman filter algorithm respectively to train the artificial neural network and forecast the road freight turnov.
文中仿真以全国历年公路货运周转量为例,分别采用BP算法和扩展卡尔曼滤波算法对神经网络进行训练,2种训练方法预测的结果对比表明扩展卡尔曼滤波训练算法具有更好的准确性和更高的运算效率。
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