Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study
| Autor: | Aws A. Magableh, Nawaf O. Alsrehin, Ahmad F. Klaib |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Government
General Computer Science business.industry Computer science General Engineering Entire globe Artificial intelligent data mining Machine learning computer.software_genre Software machine learning Traffic congestion Control system General Materials Science Data mining Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business intelligent transportation computer Intelligent transportation system lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 49830-49857 (2019) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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