Abstract

As people’s lives get better and better, more and more people choose to travel and with that comes the demand for more transportation. For now, traditional transportation hubs can temporarily meet people’s travel needs. If driven by big data concepts and methods, the various capabilities of high-speed rail transportation hubs will be sublimated, and the regional economy will be in line with the prosperity of this place. Proportionally, railway hubs are extremely attractive to the rapid growth of the regional economy. This paper takes the high-speed railway hub construction model under big data as the research object and verifies the reliability of the research model and the development of economic regions based on the high-speed railway data in recent years as reference parameters. This article selects the panel data of railway transportation and regional economy in China’s provinces for 10 consecutive years from 2011 to 2020. Among them, seven indicators were selected for railway transportation: passenger volume, freight volume, passenger turnover, cargo turnover, number of railway employees, railway transportation industry fixed asset investment and construction scale, and per capita railway network density. In terms of regional economy, six indicators were selected: regional GDP, per capita GDP, per capita investment in fixed assets, per capita total retail sales of consumer goods, per capita investment in imports and exports, and the proportion of the added value of the tertiary industry in GDP. The experimental results prove that each sample is tested in pairs, the standard error level of the mean is 0.002, which is less than 0.05, and high-speed railway construction can finally achieve economic integration. By improving the development of high-speed railways, continuously shortening the distance between time and space, breaking regional trade barriers, and reducing the cost of commodity circulation, industrial interaction and coordinated development between different regions can be effectively promoted.