Smart City Traffic Management

Abstract

In smart cities, traffic congestion is a significant challenge, leading to delays, hindrance to emergency vehicles, and localized pollution. Contributing factors include a surge in vehicles, inadequate infrastructure, system failures, and limited awareness of traffic signals. Diverse con- gestion identification techniques, such as image processing, laser tracking, and inductive loop systems, exist. However, this model centers on Infrared technology. It employs Infrared to gauge vehicle density, subsequently regulating traffic signals through ESP8266 NodeMCU, with data relayed to a central cloud system. The solution seamlessly integrates with existing models, offering rapid installation. Benefits encompass time savings for motorists, reduced traffic vio- lations, and effective congestion management, furthering emergency vehicle access and abating environmental impact. Challenges involve precision in Infrared-based density assessment, scala- bility testing, sustained maintenance, and collaboration with pertinent authorities. Real-world data and user feedback offer prospects for algorithmic refinement, while historical traffic analysis informs urban planning. Exploring Internet of Things (IoT) integration enhances its potential in reshaping urban traffic control