Abstract
In order to sketch the transport infrastructure construction in an economy or a region, the government has to predict the passenger volume, under the local policy of industrial investment. In this paper, we propose a combined input-output and distributed lag prediction model of passenger volume in a province in P. R. China, under a certain policy of industrial investment called Silk Road Economic Belt. Specifically, the relationships between the passenger volume, GDP (gross domestic product), gross output, and transportation consumption are analyzed, and then the industrial development speed analysis and classification are used to calculate the average development speeds and the GDP contributions of 42 industries. Combining the input-output table, the provincial transportation consumption under the Silk Road Economic Belt policy is predicted, and the passenger volumes of the cities and the province in the future are predicted by the distributed lag models. Considering the uncertainty of the investment, the elastic ranges of the cities and the province’s passenger volumes are determined. The results show that the correlation between the passenger volume and transportation consumption is the highest, and it is equal to 0.975. In 2020, the passenger volume in Shaanxi is 1,641,305 thousands, and the error between the predicted value and the value obtained by summing the cities’ passenger volumes is smaller than 0.002%.
Highlights
Before building the transport infrastructure in an economy or a region, the government has to predict the passenger volume under the local policy of industrial investment. e Silk Road Economic Belt is known as a strategic route that connects Asia, Europe, and Africa, which plays an important role in promoting the coordinated development of the economy and society in many regions in P
In order to better support the development of the Silk Road Economic Belt, the corresponding transport system should be established, which requires an accurate passenger volume prediction based on the data analytics under the local policy of industrial investment
E support vector machine was used to predict the passenger volume in a metropolitan area by gradually changing the parameter values of the loss function, penalty factor, and Gaussian kernel function [1]. e support vector regression (SVR) model was used to predict the subway passenger Journal of Advanced Transportation volume, and it was compared with the seasonal autoregressive integrated moving average (SARIMA) quadratic regression and linear regression [2]. e accuracy of the LSSVM-RBF neural network that combined radial basis function neural network and least squares support vector was much higher than the single model [3]
Summary
Fan Yang ,1 Zhao-guo Huang, Hua-zhi Yuan ,2 Jian-jun Wang, Wei-jia Li, and Sai Wang. In order to sketch the transport infrastructure construction in an economy or a region, the government has to predict the passenger volume, under the local policy of industrial investment. We propose a combined input-output and distributed lag prediction model of passenger volume in a province in P. R. China, under a certain policy of industrial investment called Silk Road Economic Belt. The relationships between the passenger volume, GDP (gross domestic product), gross output, and transportation consumption are analyzed, and the industrial development speed analysis and classification are used to calculate the average development speeds and the GDP contributions of 42 industries. Combining the input-output table, the provincial transportation consumption under the Silk Road Economic Belt policy is predicted, and the passenger volumes of the cities and the province in the future are predicted by the distributed lag models. In 2020, the passenger volume in Shaanxi is 1,641,305 thousands, and the error between the predicted value and the value obtained by summing the cities’ passenger volumes is smaller than 0.002%
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