Abstract
Intelligent Transport Systems (ITS) can assist in the identification and reduction of vehicular traffic congestion. In this context, this paper proposes CARTIM, a proposal for collaborative identification and minimization of vehicular congestion. CARTIM uses V2V (Vehicle-to-Vehicle) communication to cooperatively measure the local level of vehicular traffic congestion. Additionally, if any infrastructure is present, the dissemination of consolidated information may occur to vehicles in other regions through V2I (Vehicle-to-Infrastructure) communication. To effectively identify a traffic congestion locally (in vehicles), CARTIM employs a fuzzy logic-based system, which is used in the treatment of qualitative information (e.g., vehicle density etc). The proposed technique also efficiently uses the collaborative communication channel, preventing overload. The simulation results showed that CARTIM can detect congestion (better than related works) and minimize it (based on a heuristic).
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