Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study

被引:49
|
作者
Alsrehin, Nawaf O. [1 ]
Klaib, Ahmad F. [1 ]
Magableh, Aws [1 ]
机构
[1] Yarmouk Univ, Fac Informat Technol & Comp Sci, Comp Informat Syst Dept, Irbid 21136, Jordan
来源
IEEE ACCESS | 2019年 / 7卷
关键词
Artificial intelligent; data mining; intelligent transportation; machine learning; HETEROGENEOUS TRAFFIC FLOW; TRAVEL-TIME; PEDESTRIAN DETECTION; ACCIDENT PREDICTION; DRIVER BEHAVIOR; NEURAL-NETWORK; RANDOM FORESTS; ROUTE CHOICE; MODEL;
D O I
10.1109/ACCESS.2019.2909114
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Traffic congestion is becoming the issues of the entire globe. This study aims to explore and review the data mining and machine learning technologies adopted in research and industry to attempt to overcome the direct and indirect traffic issues on humanity and societies. The study's methodology is to comprehensively review around 165 studies, criticize, and categorize all these studies into a chronological and understandable category. The study is focusing on the traffic management approaches that were depended on data mining and machine learning technologies to detect and predict the traffic only. This study has found that there is no standard traffic management approach that the community of traffic management has agreed on. This study is important to the traffic research communities, traffic software companies, and traffic government officials. It has a direct impact on drawing a clear path for new traffic management propositions. This study is one of the largest studies with respect to the size of its reviewed articles that were focused on data mining and machine learning. Additionally, this study will draw general attention to a new traffic management proposition approach.
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页码:49830 / 49857
页数:28
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