Using Intelligent Vehicle Infrastructure Integration for Reducing Congestion in Smart City

Abstract

This paper proposes a novel cognitive cellular automata (CA) approach to traffic management that can adapt to immediate requirements, be applied for 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 to increase the channel-reuse rate and decrease the allocation of redundant channels. This approach provides the advantage of a heterogeneous communication interface based on cognitive mechanisms that recognize different transmission modulation modes. The receiver gets messages through different transmission modulation modes. In this work, we postulate vehicles connecting to traffic congestion computing centers by vehicle-to-roadside communications within a car society. Roadside units serve each road segment, and we suppose that every car has a navigation device. We propose an innovative congestion-reduction mechanism that provides directions to a vehicle's navigation device after the driver sets the origin location and the destination. This mechanism calculates the congestion status of the upcoming road segment. By tracking the status of road segments 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.