Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks
The primary goals of transportation agencies and researchers studying traffic operations are to ease traffic and increase road safety through the use of vehicular ad hoc networks. Agencies can't achieve their goals without reliable and consistent data on the current traffic situation. The Leve...
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| Format: | Article |
| Language: | English |
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Tikrit University
2023-06-01
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| Series: | Tikrit Journal of Pure Science |
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| Online Access: | https://tjpsj.org/index.php/tjps/article/view/1428 |
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| author | Ahmed Ibrahim Turki Saad Talib Hasson |
| author_facet | Ahmed Ibrahim Turki Saad Talib Hasson |
| author_sort | Ahmed Ibrahim Turki |
| collection | DOAJ |
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The primary goals of transportation agencies and researchers studying traffic operations are to ease traffic and increase road safety through the use of vehicular ad hoc networks. Agencies can't achieve their goals without reliable and consistent data on the current traffic situation. The Level-of-Service (LOS) index is a helpful measure of freeway traffic operations. Conventional fixed-location cameras and sensors are impractical and expensive for gathering reliable traffic density data on every road in large networks. Flow data is a new, low-cost option that has the potential to boost safety and operations. This study proposes an algorithm for hourly LOS assessment by incorporating flow data provided by the MIDAS (Motorway Incident Detection and Automatic Signaling) system. The proposed algorithm uses machine learning techniques to classify LOS data based on the flow of traffic. The input features that are subject to prediction are a group of technical indicators. The real-world LOS was determined by analyzing data from stationary sensors. The outcomes demonstrate that technical indicators can be utilized to enhance the accuracy of LOS estimation (Random Forest= 93.1, k-nearest neighbors = 92.5, and Support Vector Machine = 91.4). The current work introduces a novel approach to the selection of technical indicators and their use as features, which allows for highly accurate short-term prediction of LOS estimation.
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| format | Article |
| id | doaj-art-05936173fc844536b6b62dd70f52f593 |
| institution | Kabale University |
| issn | 1813-1662 2415-1726 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Tikrit University |
| record_format | Article |
| series | Tikrit Journal of Pure Science |
| spelling | doaj-art-05936173fc844536b6b62dd70f52f5932025-08-20T03:50:20ZengTikrit UniversityTikrit Journal of Pure Science1813-16622415-17262023-06-0128310.25130/tjps.v28i3.1428Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networksAhmed Ibrahim TurkiSaad Talib Hasson The primary goals of transportation agencies and researchers studying traffic operations are to ease traffic and increase road safety through the use of vehicular ad hoc networks. Agencies can't achieve their goals without reliable and consistent data on the current traffic situation. The Level-of-Service (LOS) index is a helpful measure of freeway traffic operations. Conventional fixed-location cameras and sensors are impractical and expensive for gathering reliable traffic density data on every road in large networks. Flow data is a new, low-cost option that has the potential to boost safety and operations. This study proposes an algorithm for hourly LOS assessment by incorporating flow data provided by the MIDAS (Motorway Incident Detection and Automatic Signaling) system. The proposed algorithm uses machine learning techniques to classify LOS data based on the flow of traffic. The input features that are subject to prediction are a group of technical indicators. The real-world LOS was determined by analyzing data from stationary sensors. The outcomes demonstrate that technical indicators can be utilized to enhance the accuracy of LOS estimation (Random Forest= 93.1, k-nearest neighbors = 92.5, and Support Vector Machine = 91.4). The current work introduces a novel approach to the selection of technical indicators and their use as features, which allows for highly accurate short-term prediction of LOS estimation. https://tjpsj.org/index.php/tjps/article/view/1428Traffic flow dataLevel-of-ServiceRandom ForestSVMKNNVANET |
| spellingShingle | Ahmed Ibrahim Turki Saad Talib Hasson Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks Tikrit Journal of Pure Science Traffic flow data Level-of-Service Random Forest SVM KNN VANET |
| title | Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks |
| title_full | Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks |
| title_fullStr | Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks |
| title_full_unstemmed | Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks |
| title_short | Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks |
| title_sort | using a new algorithm in machine learning approaches to estimate level of service in hourly traffic flow data in vehicular ad hoc networks |
| topic | Traffic flow data Level-of-Service Random Forest SVM KNN VANET |
| url | https://tjpsj.org/index.php/tjps/article/view/1428 |
| work_keys_str_mv | AT ahmedibrahimturki usinganewalgorithminmachinelearningapproachestoestimatelevelofserviceinhourlytrafficflowdatainvehicularadhocnetworks AT saadtalibhasson usinganewalgorithminmachinelearningapproachestoestimatelevelofserviceinhourlytrafficflowdatainvehicularadhocnetworks |