Edge-Enabled Smart Traffic Management System: An IoT Implementation for Urban Mobility

Abstract

Effective traffic management has emerged as a critical issue in today's rapidly urbanising areas with a rise in the number of cars. By developing an Edge-Enabled Smart Traffic Management System (EESTMS) run on the Internet of Things (IoT), this paper puts forth a novel approach. EESTMS makes use of edge computing and IoT technology's promise to improve urban transportation. An large network of thoughtfully placed sensors and cameras dispersed around the city forms the system's central structure. These gadgets continuously gather data on the volume, speed, and congestion of moving vehicles. This information offers insightful information about traffic trends. We can lessen latency and lighten the load on centralised systems by processing this data at the edge. In order to analyse the data and identify traffic bottlenecks and congestion hotspots, machine learning techniques are used. Real-time analysis allows for dynamic traffic signal adjustments, which optimise traffic flow and shorten commuter travel times. EESTMS also offers a user-friendly interface with real-time traffic information, alternate routes, and tailored navigation advice that is accessible via mobile applications and web platforms. By making wise decisions, commuters can lessen their stress and carbon footprint. EESTMS plays a critical role in advancing sustainability by reducing fuel consumption and greenhouse gas emissions through effective traffic management, in addition to enhancing urban mobility. By giving emergency vehicles priority routing, this system also improves emergency response times. The application of EESTMS has shown promising outcomes in terms of lessened traffic congestion, improved commuter experiences, and lower environmental impact. Innovative solutions like EESTMS can open the door for smarter, more sustainable urban mobility as cities continue to grow, eventually enhancing citizens' quality of life.