A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS)
In todays world, traffic congestion is a major problem in almost all metropolitans. This problem is even becoming more crucial due to increasing numbers of vehicles. Mobility of people, travel time duration, quality of life, transportation planning systems and traffic management are examples which b...
| Main Authors: | , , , |
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| Format: | Article |
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Human Resource Management Academic Research Society
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106683/ |
| _version_ | 1848864804134977536 |
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| author | Ahanin, Fatemeh Mustapha, Norwati Zolkepli, Maslina Husin, Nor Azura |
| author_facet | Ahanin, Fatemeh Mustapha, Norwati Zolkepli, Maslina Husin, Nor Azura |
| author_sort | Ahanin, Fatemeh |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In todays world, traffic congestion is a major problem in almost all metropolitans. This problem is even becoming more crucial due to increasing numbers of vehicles. Mobility of people, travel time duration, quality of life, transportation planning systems and traffic management are examples which bear the effects of traffic congestion The modern smart technology such as Artificial Intelligence (AI) has reduced traffic congestion by improving traffic monitoring and management technologies. These technologies require sufficient and accurate traffic data such as flow, velocity, and traffic density. Several machine learning-based methods have been proposed to predict the traffic state. Providing accurate prediction is an important stage in the successful implementation of Intelligent Transportation Systems (ITS). In this paper, we summarize the latest approaches in enhancing traffic state prediction, and possible developments in future, which potentially can transform many aspects of traffic management. |
| first_indexed | 2025-11-15T13:54:38Z |
| format | Article |
| id | upm-106683 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:54:38Z |
| publishDate | 2023 |
| publisher | Human Resource Management Academic Research Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1066832024-09-26T07:14:29Z http://psasir.upm.edu.my/id/eprint/106683/ A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS) Ahanin, Fatemeh Mustapha, Norwati Zolkepli, Maslina Husin, Nor Azura In todays world, traffic congestion is a major problem in almost all metropolitans. This problem is even becoming more crucial due to increasing numbers of vehicles. Mobility of people, travel time duration, quality of life, transportation planning systems and traffic management are examples which bear the effects of traffic congestion The modern smart technology such as Artificial Intelligence (AI) has reduced traffic congestion by improving traffic monitoring and management technologies. These technologies require sufficient and accurate traffic data such as flow, velocity, and traffic density. Several machine learning-based methods have been proposed to predict the traffic state. Providing accurate prediction is an important stage in the successful implementation of Intelligent Transportation Systems (ITS). In this paper, we summarize the latest approaches in enhancing traffic state prediction, and possible developments in future, which potentially can transform many aspects of traffic management. Human Resource Management Academic Research Society 2023-03-12 Article PeerReviewed Ahanin, Fatemeh and Mustapha, Norwati and Zolkepli, Maslina and Husin, Nor Azura (2023) A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS). International Journal of Academic Research in Business and Social Sciences, 13 (3). 923 - 935. ISSN 2222-6990 https://hrmars.com/index.php/IJARBSS/article/view/16683/A-Review-of-Traffic-State-Prediction-TSP-Methods-in-Intelligent-Transportation-Systems-ITS 10.6007/ijarbss/v13-i3/16683 |
| spellingShingle | Ahanin, Fatemeh Mustapha, Norwati Zolkepli, Maslina Husin, Nor Azura A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS) |
| title | A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS) |
| title_full | A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS) |
| title_fullStr | A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS) |
| title_full_unstemmed | A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS) |
| title_short | A review of Traffic State Prediction (TSP) methods in Intelligent Transportation Systems (ITS) |
| title_sort | review of traffic state prediction (tsp) methods in intelligent transportation systems (its) |
| url | http://psasir.upm.edu.my/id/eprint/106683/ http://psasir.upm.edu.my/id/eprint/106683/ http://psasir.upm.edu.my/id/eprint/106683/ |