A scientometric review of research on traffic forecasting in transportation - Jin Liu; Naiqi Wu; Yan Qiao; Zhiwu Li (IET Intelligent Transport Systems, 2021)
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.