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.

Department of Civil Engineering Islamic Azad University Tabriz 5157944533 Iran

Faculty of Engineering School of Civil Engineering Universiti Teknologi Malaysia Skudai Johor 81310 Malaysia

Faculty of Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru Johor 81310 Malaysia

Institute for Nanomaterials Advanced Technologies and Innovation Studentska 2 461 17 Liberec Czech Republic

Institute of Noise and Vibration Universiti Teknologi Malaysia City Campus Jalan Semarak Kuala Lumpur 54100 Malaysia

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