Different prediction models based on the regression analysis methods are studied in this work and they
are successfully implemented for predicting the regional freight transportation demand (RFTD). RFTD
plays an important role in reflecting the economic states, such as production improvement, economic
restructuring, and economic growth style. Thus, the prediction models for RFTD have been widely used
in many areas, such as academic and industrial domains. In this work, based on different prediction
models, several Regional Freight Transportation Demand Prediction Models (RFTDPMs) have been
constructed by using Multiple Linear Regression (MLR), Non-Linear Regression (NLR), and Simple Linear
Regression (SLR). According to the fitting efficiency, the simulation results show that the RFTDPM based
on NLR offers superior performances in predicting RFTD compared with the other regression models.
However, if the validation rates of the RFTDPMs
Different prediction models based on the regression analysis methods are studied in this work and theyare successfully implemented for predicting the regional freight transportation demand (RFTD). RFTDplays an important role in reflecting the economic states, such as production improvement, economicrestructuring, and economic growth style. Thus, the prediction models for RFTD have been widely usedin many areas, such as academic and industrial domains. In this work, based on different predictionmodels, several Regional Freight Transportation Demand Prediction Models (RFTDPMs) have beenconstructed by using Multiple Linear Regression (MLR), Non-Linear Regression (NLR), and Simple LinearRegression (SLR). According to the fitting efficiency, the simulation results show that the RFTDPM basedon NLR offers superior performances in predicting RFTD compared with the other regression models.However, if the validation rates of the RFTDPMs
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