Railway freight volume is an important index in railway transportation. This paper combined ARMA model with multivariate regression model to analyze the variables that affected the rail freight volume. Ten variables are selected as explanatory variables to study their relationship with rail freight volume. The results show that the rail freight volume is affected by many factors together and we also get the rail freight regression equation. Our results can provide the support for the traffic department to develop the transport plan.
Application of the ARMA Model in Railway Freight Volume Analysis
Fifth International Conference on Transportation Engineering ; 2015 ; Dailan, China
ICTE 2015 ; 78-85
25.09.2015
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Random Forest Regression Model Application for Prediction of China’s Railway Freight Volume
Springer Verlag | 2021
|