Virus-traffic coupled dynamic model for virus propagation in vehicle-to-vehicle communication networks

With the development of connected vehicle technology, virus propagation that exists in a traditional network environment will gradually penetrate vehicle-to-vehicle (V2V) communication networks, thus posing a serious threat to the security of intelligent transportation systems (ITS). Understanding the characteristics of virus propagation through space and time is the key to ensuring the safety of ITS. However, most existing studies on virus propagation have ignored the dynamic relationship between virus transmission and traffic flow based on the assumption that the probability of virus infection is a constant. In light of this, this study proposes a two-layer model, called the virus-traffic coupled dynamic model, to investigate virus propagation in V2V communication networks. First, the dynamics of traffic flow and vehicular mobility are formulated as many update rules between cellular automata in the lower layer; then, due to the similarity between biological epidemic dissemination and virus propagation, an epidemic model called the susceptible-infected-recovered (SIR) was built to model the virus propagation process in the upper layer; finally, the lower and upper layer are connected by the probability of virus infection. Numerical experiments show that the model can accurately reproduce the process of virus transmission over space and time. The experiments also prove that reducing the probability of virus infection can constrain the spread of the virus effectively for both different communication range limits and various traffic densities.