The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning
Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environ...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2019
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| Subjects: | |
| Online Access: | http://eprints.utm.my/90278/1/MuhammadJunaidAli2019_TheDynamicsofTrafficCongestion.pdf |
| _version_ | 1796864997063655424 |
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| author | Ali, M. J. Sheng, T. K Yusof, K. M. Suhaili, M. R. Ghazali, N. E. Ali, S. |
| author_facet | Ali, M. J. Sheng, T. K Yusof, K. M. Suhaili, M. R. Ghazali, N. E. Ali, S. |
| author_sort | Ali, M. J. |
| collection | ePrints |
| description | Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environmental issues. This has caused serious concerns among the major economies of the world, especially in Asia-Pacific region. Many countries are playing an active role in eradicating this problem and some have been quite successful so far. Malaysia, being a major ASEAN economy is also tackling with this huge problem. The authorities are committed to solve the issue. In this regard, solving the issue leveraging the use of big data analytics has become crucial. The authorities can form a complete robust framework based on big data analytics and decision making process to solve the issue effectively. The work focuses and observes the traffic data samples and analyzes the accuracy of machine learning algorithms, which helps in decision making. Yet, here is a lot to be done if the government needs to solve the problem effectively. Supposedly, a comprehensive big data transport framework leveraging machine learning, is one way to solve the issue. |
| first_indexed | 2024-03-05T20:50:17Z |
| format | Article |
| id | utm.eprints-90278 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:50:17Z |
| publishDate | 2019 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | utm.eprints-902782021-04-18T04:01:31Z http://eprints.utm.my/90278/ The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning Ali, M. J. Sheng, T. K Yusof, K. M. Suhaili, M. R. Ghazali, N. E. Ali, S. TK Electrical engineering. Electronics Nuclear engineering Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environmental issues. This has caused serious concerns among the major economies of the world, especially in Asia-Pacific region. Many countries are playing an active role in eradicating this problem and some have been quite successful so far. Malaysia, being a major ASEAN economy is also tackling with this huge problem. The authorities are committed to solve the issue. In this regard, solving the issue leveraging the use of big data analytics has become crucial. The authorities can form a complete robust framework based on big data analytics and decision making process to solve the issue effectively. The work focuses and observes the traffic data samples and analyzes the accuracy of machine learning algorithms, which helps in decision making. Yet, here is a lot to be done if the government needs to solve the problem effectively. Supposedly, a comprehensive big data transport framework leveraging machine learning, is one way to solve the issue. Institute of Advanced Engineering and Science 2019-08 Article PeerReviewed application/pdf en http://eprints.utm.my/90278/1/MuhammadJunaidAli2019_TheDynamicsofTrafficCongestion.pdf Ali, M. J. and Sheng, T. K and Yusof, K. M. and Suhaili, M. R. and Ghazali, N. E. and Ali, S. (2019) The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning. Indonesian Journal of Electrical Engineering and Computer Science, 15 (2). pp. 1086-1094. ISSN 2502-4752 http://dx.doi.org/10.11591/ijeecs.v15.i2.pp1086-1094 DOI: 10.11591/ijeecs.v15.i2.pp1086-1094 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ali, M. J. Sheng, T. K Yusof, K. M. Suhaili, M. R. Ghazali, N. E. Ali, S. The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning |
| title | The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning |
| title_full | The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning |
| title_fullStr | The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning |
| title_full_unstemmed | The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning |
| title_short | The dynamics of traffic congestion: a specific look into Malaysian scenario and the plausible solutions to eradicate it using machine learning |
| title_sort | dynamics of traffic congestion a specific look into malaysian scenario and the plausible solutions to eradicate it using machine learning |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/90278/1/MuhammadJunaidAli2019_TheDynamicsofTrafficCongestion.pdf |
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