Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

To study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python...

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Autores principales:	Hua Chen, Ming Cai, Ke Huang, Shuxin Jin
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/2e10bb535d3749b79d3426906e71e752
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id	oai:doaj.org-article:2e10bb535d3749b79d3426906e71e752
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spelling	oai:doaj.org-article:2e10bb535d3749b79d3426906e71e7522021-11-22T01:10:44ZClassification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics1875-919X10.1155/2021/2977998https://doaj.org/article/2e10bb535d3749b79d3426906e71e7522021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2977998https://doaj.org/toc/1875-919XTo study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python to process and visualize data, combined with bibliometric software VOSviewer to assist data visualization. Firstly, a preprocessing data algorithm was designed to deduplicate the collected data, merge synonyms, and extract key technologies. Then the paper records that contained the key technology lexicon were filtered out. Next, the annual number of publications and the distribution of key technologies over time were counted. The least squares method was used to fit the distribution of the annual proportion of the publications, and the slope k1 of the fitted linear regression equation was used to determine the research interest trend of key technologies. The key technologies were divided into “hot technology,” “cold technology,” and “other technologies,” according to the research heat trend. In order to further explore the research hotspots, the least squares method was also used to fit the citations of all technologies to obtain the slope k2. We use the Gaussian mixture model (GMM) algorithm to cluster k1 and k2 of each technology. As a result, the 144 technologies were divided into 13 super-key technologies, 60 key technologies, 59 relative key technologies, and 12 lower-key technologies. Then, the evolution of key technologies was analyzed from two perspectives of weighted evolution and cumulative evolution. And the technology evolution trend in the transportation field in the past 20 years was explored. Finally, the cooccurrence clustering method was adopted to divide key transportation technologies into five categories: vehicle technology and control, optimization algorithms and simulation techniques, artificial intelligence and big data, Internet of Things and computing, and communication technology. The research results can provide references for different people in the transportation field, including but not limited to researchers, journal editors, and funding agencies.Hua ChenMing CaiKe HuangShuxin JinHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Hua Chen Ming Cai Ke Huang Shuxin Jin Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics
description	To study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python to process and visualize data, combined with bibliometric software VOSviewer to assist data visualization. Firstly, a preprocessing data algorithm was designed to deduplicate the collected data, merge synonyms, and extract key technologies. Then the paper records that contained the key technology lexicon were filtered out. Next, the annual number of publications and the distribution of key technologies over time were counted. The least squares method was used to fit the distribution of the annual proportion of the publications, and the slope k1 of the fitted linear regression equation was used to determine the research interest trend of key technologies. The key technologies were divided into “hot technology,” “cold technology,” and “other technologies,” according to the research heat trend. In order to further explore the research hotspots, the least squares method was also used to fit the citations of all technologies to obtain the slope k2. We use the Gaussian mixture model (GMM) algorithm to cluster k1 and k2 of each technology. As a result, the 144 technologies were divided into 13 super-key technologies, 60 key technologies, 59 relative key technologies, and 12 lower-key technologies. Then, the evolution of key technologies was analyzed from two perspectives of weighted evolution and cumulative evolution. And the technology evolution trend in the transportation field in the past 20 years was explored. Finally, the cooccurrence clustering method was adopted to divide key transportation technologies into five categories: vehicle technology and control, optimization algorithms and simulation techniques, artificial intelligence and big data, Internet of Things and computing, and communication technology. The research results can provide references for different people in the transportation field, including but not limited to researchers, journal editors, and funding agencies.
format	article
author	Hua Chen Ming Cai Ke Huang Shuxin Jin
author_facet	Hua Chen Ming Cai Ke Huang Shuxin Jin
author_sort	Hua Chen
title	Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics
title_short	Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics
title_full	Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics
title_fullStr	Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics
title_full_unstemmed	Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics
title_sort	classification and evolution analysis of key transportation technologies based on bibliometrics
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/2e10bb535d3749b79d3426906e71e752
work_keys_str_mv	AT huachen classificationandevolutionanalysisofkeytransportationtechnologiesbasedonbibliometrics AT mingcai classificationandevolutionanalysisofkeytransportationtechnologiesbasedonbibliometrics AT kehuang classificationandevolutionanalysisofkeytransportationtechnologiesbasedonbibliometrics AT shuxinjin classificationandevolutionanalysisofkeytransportationtechnologiesbasedonbibliometrics
_version_	1718418330590642176

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

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