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Article type: Research Article
Affiliations: [a] School of Economics and Management, Chang’an University, Xi’an, China | [b] School of Economics and Management, Shaanxi College of Communication Technology, Xi’an, China
Correspondence: [*] Corresponding author. Lu Wang, School of Economics and Management, China. E-mail: luwang1618@163.com.
Abstract: With the prosperity of national economy and the development of highway construction, highway freight transportation plays an increasingly important role in the market economy. Due to its great flexible characteristic, highway freight transportation has been the main mode of transportation in China. On the macro level, there are many factors affecting the development of highway freight transportation especially under the background of the new era. Based on the historical data of the development of highway freight transportation, grey entropy analysis method is applied to analyze the significance of influencing factors for the development of highway freight transportation whose key indicator is highway freight turnover. Then GM (1, N) model is established to predict the development trend of highway freight turnover and its significant influencing factors. Finally, main problems existing in highway freight transportation and development prospect were discussed and analyzed. The research results show that the three most significant factors affecting the development of road freight turnover in China are the total state revenue, GDP and average distance of highway freight. The established GM (1, N) model can conduct high precision prediction for the development of highway freight transportation. Opportunities and challenges of highway freight transportation industry coexist and its development prospect is promising. It is expected to provide beneficial references for the development strategy and decision-making of highway freight transportation in China.
Keywords: Highway freight transportation, significance analysis, grey entropy analysis method, GM (1, N) prediction model
DOI: 10.3233/JIFS-210141
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1237-1246, 2021
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