Congestion problem in vehicular ad hoc networks (VANETs) is one of the main factors that negatively affect the inter-vehicle communication (IVC) efficiency of such emerging networks. Decentralized congestion control (DCC) is an effective method for overcoming the congestion problem. However, lower priority packets such as cooperative awareness messages (CAMs) may be encountered with the starvation problem in the DCC approach. This paper proposes FDCC: a traffic-centric Fuzzy DCC approach for improving CAMs delivery ratio, especially in congested traffic flows. FDDC architecture consists of four fuzzy inference systems (FISs). The inter-vehicle distance (IVD) and the speed variation are the spatiotemporal fuzzy inputs, which are customized for both single direction and multi-direction scenarios. Based on the FIS outputs, the FDCC control system, unlike the DCC, provides a way for processing starved CAMs by ignoring unnecessary even-driven messages. The obtained simulation results through OMNET++ in both single and multi-direction scenarios show the efficiency of FDCC in terms of the end-to-end delay, network throughput, and packet delivery ratio for event-driven messages as well as essential CAMs.