Paper
23 August 2023 The influencing factors analysis on road container transport freight index in Ningbo based on Spearman and VAR model
Yang Lin, Xinzi Wang, Rongna Xiao, Nanxi Zhao, Dachuan Ding
Author Affiliations +
Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127841R (2023) https://doi.org/10.1117/12.2691831
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China
Abstract
Road freight is not only a basic service industry for the national economy development, but also a sub-sector that the largest in size, the largest number of practitioners, the highest degree of marketisation, and people's production and life closely related to the transportation industry. Container road transportation as a main transportation modes of port distribution, is a segment market of logistics. With the development of market, road container transport freight index has been widely concerned by the whole society. This paper uses Spearman rank correlation coefficient and stationarity test, cointegration test and impulse corresponding analysis based on VAR model to explore the influencing factors on road container transport freight index with case study of Ningbo. The results show that there is a positive correlation between road container transport freight index in Ningbo and regional macro factors and operating cost factors. Additionally, the fluctuation of freight rate has a certain relationship with the influencing factors.
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Yang Lin, Xinzi Wang, Rongna Xiao, Nanxi Zhao, and Dachuan Ding "The influencing factors analysis on road container transport freight index in Ningbo based on Spearman and VAR model", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127841R (23 August 2023); https://doi.org/10.1117/12.2691831
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KEYWORDS
Roads

Autoregressive models

Transportation

Industry

Correlation coefficients

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