Smart City Gnosys

Smart city article details

Title	Trend Analysis Of Traffic Management Based On Literature Data Mining And Graph Analysis Tools
ID_Doc	58977
Authors	Ding X.; Liu W.; Wang C.; Kong D.; Tang W.; Xu R.; Zhang C.
Year	2023
Published	IET Intelligent Transport Systems, 17, 11
DOI	http://dx.doi.org/10.1049/itr2.12416
Abstract	Studites on traffic management is crucial for the development of intelligent transportation systems and smart cities. However, identifying the development stages of traffic management field based on bibliometric analysis is still lacking. In this study, CiteSpace and VOSviewer software are used to explore “traffic management” field by summarizing development process and predicting future research trend. A total of 3,028 relevant documents over the past 40 years were collected from Web of Science. Results show that (1) studies on traffic management were mainly published by researchers from USA (30.55%), China (20.90%), and some European countries; (2) the key traffic management research contents can be classified into four categories, that is, background requirements, traffic problems, method models, and control strategies; (3) the evolution process can be divided into four stages, that is, budding stage (1990–1994), development stage (1995–2003), calm stage (2004–2010), and maturation stage (2011–); (4) machine learning, deep learning and other intelligent algorithms have played more important roles in recent years, and connected vehicle management is also a potential development trend. Results suggest that cooperative vehicle-infrastructure systems or machine learning-based studies might be the hotspots on traffic management studies. © 2023 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Author Keywords	data mining; long term evolution; traffic management and control

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