客运量

客运量不断增加对国内交通部门来说是巨大的挑战，媒体将之称为“春运大考”。媒体将春运期间交通部门面临客运量大幅增加的考验称为“春运大考”（SpringFestivaltestfortransport）。

The ever-increasing passenger volume is extremely challenging to the transport sector1 nationwide , and the media have called it a " Spring Festival test for transport " .

基于BP神经网络客运量的分析及预测

Analysis and Prediction of Passenger Volume Base on BP Neural Network

GM（1,3）模型在交通系统公路客运量预测中的应用

Application of GM ( 1,3 ) in Highway Passenger Capacity Forecast of Transportation System

BP神经网络和GM（1,1）灰色模型在公路客运量预测中的应用

Application of BP Neural Network and GM ( 1,1 ) Gray Model in Road Passenger Traffic Estimation

国内航空客流增速亦在放缓；今年前5个月，中国旗舰航空公司&中国国航（AirChina）的客运量下降了将近11%。

Growth in domestic passenger traffic on flights is also decelerating ; Air China , the flagship carrier , saw traffic slump nearly 11 per cent in the year to May .

由全世界的航空公司组成的行业组织国际航空运输协会（InternationalAirTransportAssociation）估计，2015年全球航空客运量为35亿人次。

An estimated 3.5 billion people flew globally in 2015 according to the International Air Transport Association , a trade association for the world 's airlines .

许多交通研究人员只根据客运量一个因素，用GM（1,1）模型预测。

Many researchers adopt GM ( 1,1 ) model for the forecast , considering only a single element .

中国客运量最大的航空公司中国南方航空公司最近新添了一个目的地：约翰·F·肯尼迪国际机场。

China Southern Airlines , the country 's largest passenger carrier , recently added another destination : John F. Kennedy International Airport .

运输通道新建高速铁路客运量预测MD模型

MD forecasting model of passenger traffic volume for newly-built high-speed railway in transport corridor

航空业组织国际机场协会（AirportsCouncilInternational）的数据显示，上海浦东和虹桥机场的客运量在全球分列第21位和第36位。

Globally , Shanghai Pudong ranks 21st and Hongqiao ranks 36th in terms of passenger traffic , according to Airports Council International , an aviation industry association .

在分析铁路客票数据特征的基础上，提出采用分段模糊BP神经网络对铁路客运量进行数据挖掘预测。

The segment fuzzy BP Neural Network is adopted to predict the passenger traffic volume of railways in data mining based on analyzing the data feature of railway passenger tickets .

运输通道公路旅客中长距离OD模型构造及分段客运量预测

Construction of OD Model of Middle and Long Distance Highway Trips and Prediction of Segment Passenger Volume of Transport Corridor

实例证明了组合模型的预测精度高于单独的GM（1,1）模型，可以用于公路客运量预测。

Practical instance proves the estimation precision of combined model is higher than individual GM ( 1,1 ), and can be used in road passenger traffic estimation .

根据国际航空运输协会（InternationalAirTrafficAssociation）估计，未来20年全球的航空客运量将翻一番，达到73亿人次，其中主要客流来自中国，预计中国的航空客运量年均增长率将达到5.6%。

The International Air Traffic Association expects global air passenger numbers to double within 20 years to 7.3 billion , mainly due to China , where numbers will grow by an average rate of 5.6 % a year .

就在咨询机构亚太航空中心（centreforasiapacificaviation）发布上述预测的前一天，新加坡航空（singaporeairlines）公布了历史上最大的客运量月度下降数字。

The prediction from the centre for Asia Pacific Aviation , a consultancy , comes a day after Singapore Airlines recorded one of its worst monthly falls in passenger loads .

针对公路客运量预测的问题，基于常用预测方法的研究，提出BP神经网络组合预测模型。

Aiming at the forecast problem of the highway passenger quantity , and based on the research of common estimate method , a BP neural network combination forecast model was put forward .

基于GMDH的民航客运量需求预测与分析

Demand Forecasting and Analyzing of Civil Aviation Passenger Transport Markets based on GMDH

第三阶段选用了经典的Logit模型和抽象方式选择模型综合进行了客运量方式划分预测。

The third stage select classical Logit model and the abstract selection model conducting an integrated way in the passenger traffic forecast .

残差修正DGM模型在公路客运量预测中的应用

The Application of Error Remnant DGM Model on Highway Passenger Transport Volume Forecast

通过BP神经网络的建模方法把客运量的空间属性、数据属性和时间属性有机地结合起来，将数据的建模含于网络的数值当中。

The spatial attribute , data attribute and time attribute of the passenger traffic volume of railways are organically syncretized by the BP neural network modeling , and the data modeling is contained in the data of the network .

运用建立该模型预测2000&2002年的铁路客运量。结果表明：提出的LS-SVM客运量预测方法是有效的。

The prediction is conducted using passenger volume from 2000-2002.The results show that LS-SVM prediction method is an effective tool for railway passenger volume prediction .

例如，最新数据显示，过去12个月，迪拜机场（dubaiairport）的客运量和货运量分别增长了8%和17.7%。

For example , according to the latest figures , Dubai Airport registered over the past 12 months an 8 per cent and a 17.7 per cent increase in passenger and freight traffic respectively .

该模型综合考虑了GDP数据、历史汽车保有量、汽车产量、客运量、人均粗钢、人均发电、人口与公路里程对汽车保有量的影响。

The model takes into account the impact of the GDP , history automobile ownership , automobile production , passenger volume , crude steel per capita , power generation per capita , population and road mileage on car ownership .

考虑到灰色模型能够削弱随机因素的影响，论文应用通过残差修正改进的GM（1,1）灰色模型对线路客运量进行预测。

Considering that the gray model can weaken the influence of random factors , this paper presents GM ( 1,1 ) model improved through error amendment . And , it is used to forecast the route passenger volume .

并通过建立VAR模型和脉冲响应函数分析了GDP、货运量和客运量三者之间的关系；定量方面，采用神经网络对航运发展对经济关系进行了分析。

With the method of VAR , the relationship between transportation and development of economic are established through impulse response functions . For quantitative analysis , the relationship between maritime develop and GDP is analyzed by BP Neural Network .

对交通用地面积的变化影响最大的GDP，其次是社会消费品零售总额，最后是公路客运量。第三，土地利用变化对生态安全的影响研究。

GDP is the greatest impact of the changes in traffic land , followed by retail sales of consumer goods , and finally the amount of highway passenger . Third , the impacts on the ecological security by the land use changes are researched .

针对目前铁路客运量预测方法的不足，采用ε支持向量回归机（-εSVR）对铁路客运量时间序列进行预测。

Aimed at the shortcomings of the prediction methods for the passenger traffic volume of railways ,ε support vector regression is adopted to predict the time serial of the railway passenger traffic volume .

最新公布的政府采购经理人指数（pmi）出现大幅下跌，6月份车辆库存创下4年新高，航空客运量急剧下降，同时电力产出增速也有所放缓。

The latest government purchasing managers index showed a sharp decline , car inventories hit a four-year high in June , air passenger traffic has slowed sharply , and electricity output growth has also dropped .

前5年的客运量用来预测第6年的客运量，由1985&1999年的客运量建立LS-SVM客运量预测模型。

The passenger volume in the past 5 years is used to predict the passenger volume in the 6th year . The passenger volume from 1985 to 1999 is applied to build LS-SVM model for passenger volume prediction .

分析-εSVR原理，对1980&1998年的铁路客运量进行归一化处理，建立铁路客运量时间序列SVR预测模型，并进行仿真试验。

ε - SVR theory is analyzed . The railway passenger traffic volume from 1980 to 1998 is normalized . The time serial ε - SVR prediction model of railway passenger traffic volume is set up and simulation experiment is carried out .