Theme Classification and Analysis of Core Articles Published in IEEE Transactions on Intelligent Transportation Systems From 2010 to 2013

Tang, Shaohu; Li, Zhengxi; Chen, Dewang; Chen, Zhaomeng; Liu, Wenli; Liu, Xiaoming; Li, Lingxi; Shi, Xiaobo

doi:10.1109/tits.2014.2365631

Cited by 9 publications

(2 citation statements)

References 78 publications

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“…Wang et al [21], based on articles of IEEE Transactions on Intelligent Transportation Systems (2010-2013), studied the productivity and collaboration models of Intelligent Transportation Systems. Tang et al [22] classified the subject categories of different research fields of core articles in IEEE Transactions on Intelligent Transportation Systems (2010-2013) by coword analysis, including vehicle control technology, modeling, simulation, and image processing. Moral-Muoz et al [23] used highly cited literature to study scientific participants who have made significant contributions to the development of Intelligent Transportation Systems.…”

Section: Introductionmentioning

confidence: 99%

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

Chen

Cai

Huang

et al. 2021

Scientific Programming

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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.

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Section: Introductionmentioning

confidence: 99%

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

Chen

Cai

Huang

et al. 2021

Scientific Programming

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“…In this regard, there are bibliometric analyses of ITS in literature based on different approaches, such as intellectual structure [14][15][16][17][18], publications [19,20], authorship [21][22][23][24], and journals [25], or different areas within the transport field, such urban smart mobility [26], automotive technologies [27,28], and automotive supply chain [29]. However, the abovementioned researches are focused on a specific topic, which highlights the opportunity for a holistic approach that ensures consideration of all themes, their forms, and components included in the literature.…”

Section: Introductionmentioning

confidence: 99%

Visualizing the Intellectual Structure and Evolution of Intelligent Transportation Systems: A Systematic Analysis of Research Themes and Trends

et al. 2020

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According to the United Nations, 70% of the world’s population will live in cities by 2050. This growth will be reflected in the demand for better services that should be adjusted to the collective and individual needs of the population. Governments and organizations are working on defining and implementing strategies that will enable them to respond to these challenges. The main challenges are related to transport and its management, considering transportation as a core issue in the economy, sustainability, and development of the regions. In this way, the Intelligent Transportation Systems (ITS) play a key role in the response to these scenarios, being that they are the framework where the new hardware and software tools are integrated, allowing an efficient development of transportation systems management, attending to aspects such as: traffic management, communications between vehicles and infrastructures, and security, among others. Nevertheless, the concept of ITS evolves rapidly so it is necessary to understand its evolution. To do that, the current research develops a thematic analysis of ITS in literature, evaluating the intellectual structure and its evolution using SciMAT, quantifying the main bibliometric performance indicators, and identifying the main research areas, authors, journals, and countries. To this purpose, the publications related to ITS from 1993 to 2019 available in the Web of Science (WoS) Core Collection were retrieved (7649 publications) and analyzed. Finally, one of the main results is the latest research themes map of ITS, considering its intellectual structure, evolution, and relationship. It assists in the definition and implementation of strategies, the identification of the scientific, academic, and business opportunities, and future research lines to consolidate the role of ITS in the new city models.

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Analyzing Highly Cited Papers in Intelligent Transportation Systems

Moral-Muñoz

Cobo

Chiclana

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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Abstract-Citation classics offer an outlook on those papers that have attracted great and historical interest by a research community, and that could also be considered as the basis of the research field. A new approach, called H-Classics, has been developed to identify such highly cited papers. It is based on the H-index and is sensitive to both the own characteristics of the corresponding research discipline and its evolution. The present study provides a useful insight into the development of the Intelligent Transport Systems research field, revealing those scientific actors (authors, countries and institutions) that have made the biggest research contribution to its development.

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Theme Classification and Analysis of Core Articles Published in IEEE Transactions on Intelligent Transportation Systems From 2010 to 2013

Cited by 9 publications

References 78 publications

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

Visualizing the Intellectual Structure and Evolution of Intelligent Transportation Systems: A Systematic Analysis of Research Themes and Trends

Analyzing Highly Cited Papers in Intelligent Transportation Systems

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