학술논문

Trend analysis of traffic management based on literature data mining and graph analysis tools
Document Type
article
Source
IET Intelligent Transport Systems, Vol 17, Iss 11, Pp 2115-2130 (2023)
Subject
data mining
long term evolution
traffic management and control
Transportation engineering
TA1001-1280
Electronic computers. Computer science
QA75.5-76.95
Language
English
ISSN
1751-9578
1751-956X
Abstract
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.