## 1 Introduction  
Heterogeneous vehicular networks (HETVNETs) are a subclass of ad hoc networks, and differ in characteristics from other networks as vehicles are the nodes that can compute, sense, and communicate via various access technologies, such as DSRC (Dedicated Short-Range Communications), 3G (Third Generation), UMTS (Universal Mobile Telecommunications System) etc. [1, 2]. The environment is highly dynamic in nature due to the varying node positions, which causes sparse distribution of vehicles and intermittent connectivity among them, and consequently, messages are lost or delayed [3]. To ensure reliable content dissemination in HETVNETs, solutions such as name-based communication and in-network caching were proposed [4]. Each and every network node in this vehicular environment has to carry, store, and forward data with cooperative behavior. However, the \"store-carry-forward\" solution may cause more delay and increase overhead communication. Therefore, we need the classification and prediction of traffic to obtain information about future traffic conditions, and to adapt dissemination/routing protocol parameters based on these conditions.  
Although previous studies have provided intelligent transportation systems with fundamental prediction methods, this field still lacks a classification and prediction method that can be applied to various traffic conditions, and which can produce accurate results in a short computation time. Related traffic classification and prediction methods can be categorized as parametric and non-parametric. Parametric methods have a finite number of parameters, and include linear support vector machines, Bayesian networks, neural networks, and port-based, IP-based, payload-based, behavior-based, and flow-based algorithms. Non-parametric methods have an infinite number of parameters, and include decision trees, k-means algorithms, and semi-supervised learning algorithms. Research has shown that non-parametric methods produce more accurate results than parametric methods because the infinite number of parameters assists in capturing the non-linearity of traffic flow [5].  
This paper proposes a prediction model based on support vector machines (SVMs). SVMs are a non-parametric supervised machine learning technique that use associated learning algorithms to analyze data and recognize patterns [6]. SVMs are easily deployed through the mapping of non-linear data to high-dimensional feature space, and acquire a non-linear decision boundary in input space. The use of kernels twists support vector methods, and allows them to deal with other complex algorithms. Therefore, we adopted SVMs with a radial basis function (RBF) kernel for training them, as it has high performance in linear and non-linear SVMs. The contribution of this paper is a classification and prediction method that can be applied to various traffic conditions and achieve accurate results in a short computation time. The method was evaluated using a real dataset from Cambridge University (CRAWDAD, Cambridge, UK), and outperformed other methods. Although the method was tested offline, it can be used in a cloud system connected to HETVNETs. Traffic data are sent to vehicles continuously by, for example, roadside units. The routing/broadcasting protocols can utilize these data to adapt the dissemination of messages to traffic conditions, and consequently, message delay or loss can be avoided.  
This paper is structured as follows: the first section presents the introduction of the paper. The second section shows the works related to the proposed model. The third section explains the theoretical background behind the proposed model to show that the model is based on a scientific framework. The fourth section shows the proposed model and how it is applied. The fifth section explains how the proposed model is accommodated to a real traffic dataset, and presents the results of using this model. Finally, the sixth section concludes the paper and outlines future directions.