SMART TRANSPORTATION SYSTEMS: IOT-CONNECTED WIRELESS SENSOR NETWORKS FOR TRAFFIC CONGESTION MANAGEMENT

Abstract

Smart Transportation Systems (STS) are crucial to alleviating urban traffic congestion. This paper examines how gridlock managers might use IoT-related remote sensor networks to improve transportation productivity and flexibility. The study's initial inquiry examines traffic congestion's negative consequences on cities, including increased travel time, fuel consumption, and pollution. It emphasizes the need for creative solutions to reduce traffic and improve urban life. The solution's IoT-enabled wireless sensor networks simplify real-time data collection and analysis. A dense sensor network at important traffic sites can collect significant data on traffic flow, vehicle density, and road conditions. This data enables smart traffic management methods and better transportation systems. Sensor hubs, information transmission standards, and information analysis methodologies are examined in the exploratory article. It discusses network-sending challenges such as power productivity, flexibility, and information security and proposes solutions. The essay also considers synergies with autonomous cars, smart traffic signal systems, and IoT-connected wireless sensor networks in transportation infrastructure. These pairings boost gridlock executives' viability and STS's future. An IoT-associated remote sensor network was dispatched to a metropolitan region in the exploration piece to test the proposed configuration. The research examines the data, how traffic management tactics were applied, and how traffic flow, trip time, and environmental sustainability improved. This research shows that IoT-connected wireless sensor networks may transform smart transportation system traffic congestion management. Advanced analytics and real-time data may help cities reduce congestion, increase mobility, and develop sustainable cities.