The Adaptive Recommendation Segment Mechanism to Reduce Traffic Congestion in Smart City

This paper proposes a novel cognitive cellular automata (CA) approach that can adapt to immediate requirements, spread to use in cross-area car societies, enhance system performance, and decrease traffic-congestion problems. We propose a mechanism that operates in a cognitive radio mode for increasing the channel-reuse rate and decreasing the consumption of redundant channels. The advantage is a heterogeneous-communication interface available through cognitive mechanisms that can recognize different transmission modulation modes. The receiver will get messages through different transmission modulation modes. In this work, we consider vehicles connecting to traffic-congestion computing centers (TCCCs) by vehicle-to-roadside (V2R) communications under a car society. Roadside units serve each segment, and we suppose that every car has a navigation device. We propose an innovative congestion-reducing mechanism that can help vehicles get directions with the help of a navigation device after drivers set the origin location and the destination location. This mechanism can calculate the congestion status of the upcoming segment of road. By tracking roadway segments´ status from a point of origin to a destination, our proposed mechanism can handle cross-area car societies. The current study evaluates this approach´s performance by conducting computer simulations. Simulation results reveal the strengths of the proposed CA mechanism in terms of increased lifetime and increased congestion-avoidance for urban vehicular networks.