Analytical modeling of vehicle-to-vehicle communication using spread of infection models

In this study we propose a macroscopic analytical model for modeling the vehicle-to-vehicle (V2V) communication process. Real-time information propagation by V2V communication is part of the Vehicle Infrastructure Integration (VII) initiative, aimed at improving the traffic conditions on existing roadways. Vehicles communicate among themselves and pass information regarding congestion, unsafe pavement condition or accidents. This proposed information propagation model is based on the Susceptible-Infected-Removed (SIR) model that is used to model the spread of epidemics in a region. As an analogy, we call the vehicles that have received the signal as 'infected vehicles', and those instrumented vehicles that have not received the information are called 'susceptible vehicles'. The proposed model predicts the number of infected vehicles present on the roadway at every instant of time. The model is developed for a variety of traffic conditions including different volumes, market share of instrumented vehicles, speed limits and number of lanes. Finally, it is validated using simulation results obtained from Paramics, traffic micro-simulation software.