A scientometric review of research on traffic forecasting in transportation
Abstract Research on traffic forecasting in transportation has received worldwide concern over the past three decades. While there are comprehensive review studies on traffic forecasting, few of them explore the research advancement in this field from a visual perspective. With the help of CiteSpace...
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2021-01-01
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Series: | IET Intelligent Transport Systems |
Online Access: | https://doi.org/10.1049/itr2.12024 |
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doaj-453560cd08e94e7b910f5e2f416c85ce2021-07-14T13:20:55ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-01-0115111610.1049/itr2.12024A scientometric review of research on traffic forecasting in transportationJin Liu0Naiqi Wu1Yan Qiao2Zhiwu Li3Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology Macao 999078 ChinaInstitute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology Macao 999078 ChinaInstitute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology Macao 999078 ChinaInstitute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology Macao 999078 ChinaAbstract Research on traffic forecasting in transportation has received worldwide concern over the past three decades. While there are comprehensive review studies on traffic forecasting, few of them explore the research advancement in this field from a visual perspective. With the help of CiteSpace and VOSviewer, this study uses scientometric review to identify the evolution and emerging trends of the research in the field. Totally, 1536 bibliographic records with references are extracted from Web of Science and used as the datasets to form the author network, institutional network, keyword network, and co‐citation network. The visualization of the results characterizes the research progress in the field. It can be found that Eleni I. Vlahogianni receives the highest citation frequency, China and the United States contribute most of the journal articles. Some influential institutions and articles are also identified. With the author keyword network, the words “recurrent neural network”, “convolutional neural network”, “spatio‐temporal correlation”, “traffic pattern”, and “feature selection” are identified as the emerging trends. Also, the document citation bursts reveal that the applications of combined models and the study of traffic flow forecasting in atypical situations are becoming the emerging trends. This study provides a valuable reference for the research community in this field.https://doi.org/10.1049/itr2.12024 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jin Liu Naiqi Wu Yan Qiao Zhiwu Li |
spellingShingle |
Jin Liu Naiqi Wu Yan Qiao Zhiwu Li A scientometric review of research on traffic forecasting in transportation IET Intelligent Transport Systems |
author_facet |
Jin Liu Naiqi Wu Yan Qiao Zhiwu Li |
author_sort |
Jin Liu |
title |
A scientometric review of research on traffic forecasting in transportation |
title_short |
A scientometric review of research on traffic forecasting in transportation |
title_full |
A scientometric review of research on traffic forecasting in transportation |
title_fullStr |
A scientometric review of research on traffic forecasting in transportation |
title_full_unstemmed |
A scientometric review of research on traffic forecasting in transportation |
title_sort |
scientometric review of research on traffic forecasting in transportation |
publisher |
Wiley |
series |
IET Intelligent Transport Systems |
issn |
1751-956X 1751-9578 |
publishDate |
2021-01-01 |
description |
Abstract Research on traffic forecasting in transportation has received worldwide concern over the past three decades. While there are comprehensive review studies on traffic forecasting, few of them explore the research advancement in this field from a visual perspective. With the help of CiteSpace and VOSviewer, this study uses scientometric review to identify the evolution and emerging trends of the research in the field. Totally, 1536 bibliographic records with references are extracted from Web of Science and used as the datasets to form the author network, institutional network, keyword network, and co‐citation network. The visualization of the results characterizes the research progress in the field. It can be found that Eleni I. Vlahogianni receives the highest citation frequency, China and the United States contribute most of the journal articles. Some influential institutions and articles are also identified. With the author keyword network, the words “recurrent neural network”, “convolutional neural network”, “spatio‐temporal correlation”, “traffic pattern”, and “feature selection” are identified as the emerging trends. Also, the document citation bursts reveal that the applications of combined models and the study of traffic flow forecasting in atypical situations are becoming the emerging trends. This study provides a valuable reference for the research community in this field. |
url |
https://doi.org/10.1049/itr2.12024 |
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