(Created page with " == Abstract == With the rapid expansion of the railway represented by high-speed rail (HSR) in China, competition between railway and aviation will become increasingly commo...")
 
m (Scipediacontent moved page Draft Content 381685643 to Qi et al 2019a)
 
(No difference)

Latest revision as of 10:36, 15 February 2021

Abstract

With the rapid expansion of the railway represented by high-speed rail (HSR) in China, competition between railway and aviation will become increasingly common on a large scale. Beijing, Shanghai, and Guangzhou are the busiest cities and the hubs of railway and aviation transportation in China. Obtaining their supply configuration patterns can help identify defects in planning. To achieve that, supply level is proposed, which is a weighted supply traffic volume that takes population and distance factors into account. Then supply configuration can be expressed as the distribution of supply level over time periods with different railway stations, airports, and city categories. Furthermore, nonnegative tensor factorization (NTF) is applied to pattern recognition by introducing CP (CANDECOMP/PARAFAC) decomposition and the block coordinate descent (BCD) algorithm for the selected data set. Numerical experiments show that the designed method has good performance in terms of computation speed and solution quality. Recognition results indicate the significant pattern characteristics of rail&ndash

air transport for Beijing, Shanghai, and Guangzhou are extracted, which can provide some theoretical references for practical policymakers.

Document type: Article

Full document

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document

Original document

The different versions of the original document can be found in:

https://doaj.org/toc/2071-1050 under the license cc-by
https://www.mdpi.com/2071-1050/11/6/1803/pdf,
https://ideas.repec.org/a/gam/jsusta/v11y2019i6p1803-d217090.html,
https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:6:p:1803-:d:217090,
https://academic.microsoft.com/#/detail/2924201152
http://dx.doi.org/10.3390/su11061803
under the license https://creativecommons.org/licenses/by/4.0/
Back to Top

Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.3390/su11061803
Licence: Other

Document Score

0

Views 1
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?