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Optimal competition strategy analysis of China Railway Express based on evolutionary game theory.

作者信息

Feng Fenling, Li Xiaojie, Liang Junkai, Liu Yang, Liu Chengguang

机构信息

College of Transportation Engineering, Central South University, Changsha, Hunan, China.

出版信息

PLoS One. 2022 Mar 18;17(3):e0256326. doi: 10.1371/journal.pone.0256326. eCollection 2022.

DOI:10.1371/journal.pone.0256326
PMID:35302993
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8932592/
Abstract

In the context of the Belt and Road Initiative, China Railway Express achieved rapid development. Determining how to achieve effective marking and determining how to adopt the optimal competitive strategy are the main challenges for CR Express operators. By choosing long-distance transportation as the research object, this research established a competitive model between CR Express and maritime transportation based on game theory. First, we determined the participants of this competition. By dividing common goods into the categories of high-value and high-time-sensitiveness, high-value and low-time-sensitiveness, low-value and high-time-sensitiveness, and low-value and low-time-sensitiveness, the model was used to simulate four situations and to obtain optimal strategies for operators. For CR Express, it was always effective to adopt a service optimizing strategy to improve the service level and earn market share. For maritime transportation, this strategy was effective only for high-value and low-time-sensitiveness goods as well as low value and low-time-sensitiveness goods. Therefore, transportation service suppliers should make competitive strategies based on different good types, and it is effective to use differentiation strategies to earn market share and improve competitiveness only for suitable types of goods.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/a45a2d686e84/pone.0256326.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/4c1145e6e5f1/pone.0256326.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/f50ff7757544/pone.0256326.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/8733cdb4e0a1/pone.0256326.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/401a233b5ac6/pone.0256326.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/6a2aa87c3cdc/pone.0256326.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/0ace01f7bc9c/pone.0256326.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/202447af2ab9/pone.0256326.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/541f9318565a/pone.0256326.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/b94a2cd15984/pone.0256326.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/a45a2d686e84/pone.0256326.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/4c1145e6e5f1/pone.0256326.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/f50ff7757544/pone.0256326.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/8733cdb4e0a1/pone.0256326.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/401a233b5ac6/pone.0256326.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/6a2aa87c3cdc/pone.0256326.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/0ace01f7bc9c/pone.0256326.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/202447af2ab9/pone.0256326.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/541f9318565a/pone.0256326.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/b94a2cd15984/pone.0256326.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17a9/8932592/a45a2d686e84/pone.0256326.g010.jpg

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