The current situation of the railway passenger traffic (RPT) and the traffic marketing is analyzed. The grey model theory is adopted to establish a prediction model for the railway passenger traffic volume (RPTV).T...The current situation of the railway passenger traffic (RPT) and the traffic marketing is analyzed. The grey model theory is adopted to establish a prediction model for the railway passenger traffic volume (RPTV).The RPTV from 2001 to 2005 is predicted with the proposed model, and a few suggestions are put forward.展开更多
In this study,a model based on multiple regression analysis is developed to forecast the tourism traffic volume of theme parks.First,the macro,meso and micro factors affecting traffic passenger volume are analysed.Sec...In this study,a model based on multiple regression analysis is developed to forecast the tourism traffic volume of theme parks.First,the macro,meso and micro factors affecting traffic passenger volume are analysed.Second,SPSS software is used for multivariate regression analysis on data for 10 theme parks from 2014.A tourism traffic volume forecasting model is then proposed.Finally,related data for 2015 is used to validate the model,with results showing a prediction error of 14.1%.All results show that the model has a high predictive ability.展开更多
文摘The current situation of the railway passenger traffic (RPT) and the traffic marketing is analyzed. The grey model theory is adopted to establish a prediction model for the railway passenger traffic volume (RPTV).The RPTV from 2001 to 2005 is predicted with the proposed model, and a few suggestions are put forward.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY15E080008)the National Natural Science Foundation of China(Grant No.50908205).
文摘In this study,a model based on multiple regression analysis is developed to forecast the tourism traffic volume of theme parks.First,the macro,meso and micro factors affecting traffic passenger volume are analysed.Second,SPSS software is used for multivariate regression analysis on data for 10 theme parks from 2014.A tourism traffic volume forecasting model is then proposed.Finally,related data for 2015 is used to validate the model,with results showing a prediction error of 14.1%.All results show that the model has a high predictive ability.
