Design and Implementation of an Intelligent Transportation System based on SUMO Hybrid Communication Networks

The growing demand for transportation, driven by rapid urbanization and rising vehicle numbers, has led to severe traffic congestion in cities. Thus, there are numerous limitations to traditional fixed-time signal systems in effectively managing variable traffic conditions. The aim of this research is to create a cost-effective Intelligent Transportation System (ITS) that would combine computer and communications technologies in an effort to help manage traffic, provide safety, and improve operational efficiency in the city. The system utilized real-time data that was gathered on the roads by placing sensors on the roads with IoT (Internet of Things) modules, an algorithm at the central control to provide the timing of adaptive signals and optimization of traffic. System authentication was achieved through simulations and modeling that was done using MATLAB and SUMO (Simulation of Urban Mobility)>Compared with a traditional fixed-time system, the proposed ITS resulted in a 46% lower average delay per vehicle, a 52% reduction in queue lengths, and a 77% increase in traffic volume. Our results also showed that our data transmissions had an accuracy rate of 97 percent, with an average system response time of approximately 2.1 seconds. Unlike many other ITS models that rely on separate components or expensive hardware infrastructure, the proposed system uses modularity, open-source simulation software, and hybrid communication as a single, adaptive framework that can be implemented in both developed and developing regions of cities. The paper, therefore, indicates that the intelligent transportation systems will transform the urban traffic networks into efficient and less polluting networks. It has also been stated that the research has laid the groundwork for smart cities and sustainable transportation systems because it provides a cost-effective and scalable traffic management solution. In addition, it has enabled policy-makers, transport engineers, and urban planners to make important conclusions about developing smart cities and sustainable mobility.