Influential Factor Analysis and Prediction on Initial Metro Network Ridership in Xi’an, China

Abstract

To satisfy the adaptability of forecasting the short-term and abrupt volume of the initial metro network, we build the multiple enter linear regression (MELR) model to explore the determinants and forecast the intensity during the twice expansion of the initial metro network in Xi’an. We further compare the prediction of the metro transport capacity between the MELR models with exponential smoothing and autoregressive integrated moving average (ARIMA) models. Results show that the passenger intensity significantly fluctuates with the months and days, and MELR model is more adapted for the short-term prediction of the abrupt volume than the ARIMA model during the new metro line opening and the old line expands, which avoids the drawback of time series models that need a huge database. This study provides a guide for the prediction of initial metro network volume and accurate purchase of the rail vehicles during the metro planning and expends stages.