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.
Highlights
Rapid economic growth has led to a large amount of transportation demand, and differences in travel and service requirements have gradually diversified transportation and established a complete transportation hub. is is consistent with the ideas and methods of big data
E development of railway transportation abroad has existed for a long time, and the research on the relationship between railway transportation and regional economic development is relatively mature
Based on the research results at home and abroad, this paper finds that the algorithm of each method is based on different research data. is paper constructs a high-speed railway regional economic development model and studies route planning and high-speed railway integration models for big data. is paper conducts cluster analysis on three types of data and analyzes that the overall dimensionality reduction based on four weighting algorithms can effectively remove the attributes of less important research objects
Summary
Rapid economic growth has led to a large amount of transportation demand, and differences in travel and service requirements have gradually diversified transportation and established a complete transportation hub. is is consistent with the ideas and methods of big data. How to use big data ideas and technologies to effectively use this information to serve the construction, management, and operation of nodes has become an important issue for improving the service level of integrated transmission nodes. Based on the status quo of China’s railway transportation and economic development, foreign theories and methods are used to conduct relevant research and analysis in different regions. Is paper constructs a high-speed railway regional economic development model and studies route planning and high-speed railway integration models for big data. 2. Cluster Coordination between High-Speed Railway Transportation Hub Construction and Regional Economy Based on Big Data. Information management can further highlight the benefits of various transportation, high-speed railway, effective and targeted solutions for node management problems, and improving the service level of the transmission system. Summarize the secondary data, and save their clustering information with appropriate statistics
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