## 1 Introduction  
In an IoV and VSN, vehicles act as senders, receivers and routers to broadcast data to a network or transportation agency as part of an integrated Intelligent Transportation System (ITS) [1]. The collected data can be used for traffic flow prediction to ensure safe, free-flow of traffic in metropolitan areas. The application of sensor networks as a roadside communication infrastructure is regularly used in various current intelligent transportation and smart highway systems. The roadside units (RSUs) offer a secure infrastructure along the road which are responsible for broadcasting periodic safety messages to road users. Typically, RSUs are located every 300 m to 1 km and transmit data at the interval of every 300 ms. Therefore, placing RSUs along a long stretch of highway to offer ubiquitous connectivity is not economically viable. Hence, vehicles should be able to use other vehicles to transmit and receive driver critical data feeds with limited support from fixed road side infrastructures [1; 2]. In this paper, we developed smart prediction scheme for vehicle-to-vehicle (V2V) communication [3; 4], where the vehicles can obtain predicted information using their on-board units (OBUs) which is computed by RSUs. A basic scenario of content delivery to vehicles at various ranges through vehicle-to-roadside (V2R) links is presented in Figure 1. The main operational functions of the real time prediction system depicted includes: traffic data archiving, traffic pattern processing and traffic flow forecasting. Traffic data would be collected by RSUs for purposes of data analysis. Traffic pattern processing would create a dynamic traffic pattern (TP) matrix using the collected data to assess traffic volume. This paper focuses on developing the traffic flow forecasting unit which uses the TP matrix for constructing a traffic flow prediction model. The main challenge is that short-term traffic estimations may be inaccurate because of unpredictable disruptions such as accidents on the road. Historical traffic flow data should be used for traffic time estimation in a network. Nevertheless, activity time forecast cannot exclusively depend on past movement information because of the following reasons: (1) On-street disruptions and accidents which would affect traffic flows in the network, the impact of which cannot be anticipated; (2) off-road events can affect traffic flows and they cannot be incorporated into the typical historical traffic time information; and finally, (3) traffic information is not accessible for all connections in a traffic network due to the fact that most connections are not equipped with traffic sensors. Accurately anticipating traffic time is an imperative element of IoV and intelligent transportation frameworks [5; 6]. There are a wide range of traffic time prediction techniques incorporating time arrangement examination [7; 8], Bayesian systems [9], neural networks (NNs) [10; 11; 12; 13], fuzzy systems [2], fuzzy NNs [14; 15], nonparametric regression (NP) [16; 17], and other computational intelligence approaches [18]. The availability of travel time data is increasingly being used for modelling traffic behaviour to assist road users and city authorities to make better informed decisions about travel choices, levels of pollution and congestion, the effect on public and private transportation policies, and effective repair and maintenance of the road network. However, data can often be missing for  
Figure 1: Delivery of content to vehicles via vehicle-to-roadside (v2r) links. specific timeframes due to noise in the reading or corrupted data [19; 20; 21; 22]. Various machine learning, probabilistic and statistical modelling approaches have attempted to solve the problem of missing data in traffic forecasting [23; 24; 25; 26; 27; 28; 29]. A study by [PERSON] et al. [30] showed a travel time forecasting model based on a neural system for handling missing traffic information while in [PERSON] et al.