Managing Passenger Volume from Long-Term Memory during Urban-Rural Integration Process: A Bivariate ARFIMA Analysis

With the implementation of “transport power” strategy and the 14th “Five-Year” plan in China, urban-rural integration (URI) and rural revitalization has become the emphasis. To accommodate the long-term memory and correlation between passenger volume back-and-forth urban and rural areas, a bivariate autoregressive fractionally integrated moving average (ARFIMA) model was presented to predict highway passenger volume, in which ARFIMA model can address the long-term memory and bivariate feature can accommodate the correlation. First, the ARFIMA model was adopted to predict passenger flow from urban to rural and from rural to urban area separately, and then the results were compared with those of the bivariate ARFIMA model, which estimated passenger flow from urban to rural and from rural to urban area simultaneously. The proposed model was validated by using Wuhan Statistical Yearbook 2008−2020 maintained by Wuhan Municipal Bureau of Statistics. The results revealed that the ARFIMA model with the long-term memory can predict the passenger volume simultaneously, and the prediction accuracy of the proposed model is better than that of the singular models. The findings make an alternative for predicting passenger volume and provide potential insights into corresponding policy making during URI process.