A Protocol for Identification and Minimization of Traffic Congestion in Vehicular Networks

Traffic congestion is handled by government agencies as an urban mobility problem, which generates stress to drivers and economic loss. Intelligent transport systems can assist in the identification and reduction of vehicular traffic congestion. In this context, this paper proposes CARTIM, a protocol for collaborative identification and minimization of vehicular congestion. The protocol uses V2V (Vehicle-to-Vehicle) communication to cooperatively measure the local level of vehicular traffic congestion. Additionally, if any infrastructure is present, consolidated information dissemination may occur to vehicles in other regions through V2I (Vehicle-to-Infrastructure) communication. To effectively identify a traffic congestion locally, CARTIM employs a fuzzy logic-based system, which is used in the treatment of qualitative information (vehicle density etc). The protocol also efficiently uses the collaborative communication channel, preventing overload. The simulation results showed that CARTIM can detect congestion (better than related work) and minimize it.