A review on vehicle classification and potential use of smart vehicle-assisted techniques
Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify th...
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2020
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Online Access: | http://eprints.utm.my/id/eprint/93946/1/NorhishamBakhary2020_AReviewonVehicleClassificationandPotentialUse.pdf http://eprints.utm.my/id/eprint/93946/ http://dx.doi.org/10.3390/s20113274 |
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my.utm.939462022-02-28T13:18:42Z http://eprints.utm.my/id/eprint/93946/ A review on vehicle classification and potential use of smart vehicle-assisted techniques Shokravi, Hoofar Shokravi, Hooman Bakhary, Norhisham Heidarrezaei, Mahshid Koloor, Seyed Saeid Rahimian Petr°u, Michal TA Engineering (General). Civil engineering (General) Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques. MDPI AG 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/93946/1/NorhishamBakhary2020_AReviewonVehicleClassificationandPotentialUse.pdf Shokravi, Hoofar and Shokravi, Hooman and Bakhary, Norhisham and Heidarrezaei, Mahshid and Koloor, Seyed Saeid Rahimian and Petr°u, Michal (2020) A review on vehicle classification and potential use of smart vehicle-assisted techniques. Sensors (Switzerland), 20 (11). pp. 1-29. ISSN 1424-8220 http://dx.doi.org/10.3390/s20113274 |
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TA Engineering (General). Civil engineering (General) Shokravi, Hoofar Shokravi, Hooman Bakhary, Norhisham Heidarrezaei, Mahshid Koloor, Seyed Saeid Rahimian Petr°u, Michal A review on vehicle classification and potential use of smart vehicle-assisted techniques |
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Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques. |
format |
Article |
author |
Shokravi, Hoofar Shokravi, Hooman Bakhary, Norhisham Heidarrezaei, Mahshid Koloor, Seyed Saeid Rahimian Petr°u, Michal |
author_facet |
Shokravi, Hoofar Shokravi, Hooman Bakhary, Norhisham Heidarrezaei, Mahshid Koloor, Seyed Saeid Rahimian Petr°u, Michal |
author_sort |
Shokravi, Hoofar |
title |
A review on vehicle classification and potential use of smart vehicle-assisted techniques |
title_short |
A review on vehicle classification and potential use of smart vehicle-assisted techniques |
title_full |
A review on vehicle classification and potential use of smart vehicle-assisted techniques |
title_fullStr |
A review on vehicle classification and potential use of smart vehicle-assisted techniques |
title_full_unstemmed |
A review on vehicle classification and potential use of smart vehicle-assisted techniques |
title_sort |
review on vehicle classification and potential use of smart vehicle-assisted techniques |
publisher |
MDPI AG |
publishDate |
2020 |
url |
http://eprints.utm.my/id/eprint/93946/1/NorhishamBakhary2020_AReviewonVehicleClassificationandPotentialUse.pdf http://eprints.utm.my/id/eprint/93946/ http://dx.doi.org/10.3390/s20113274 |
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1726791457913700352 |
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13.209306 |