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 (Vehicleto-
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).