Dissanayake, UWeerasekara, DSumanasekara, HIshara, DWijesiri, PMoonamaldeniya, M2026-03-192025979-833153098-3https://rda.sliit.lk/handle/123456789/4852Urban traffic management at pedestrian crossings presents considerable issues, such as pedestrian safety, congestion, and effective prioritizing of emergency vehicles. Traditional traffic signal systems are frequently static, unable to respond to real-time changes in pedestrian flow, vehicle density, and environmental variables. To overcome these issues, an IoT-based adaptive pedestrian crossing system, "IntelliCross,"is presented. The system detects emergency vehicle sirens using sound sensors and automatically adjusts pedestrian signals to green to prioritize emergency vehicle passage, resulting in faster response times and shorter delays. Furthermore, machine learning algorithms alter signal timings based on real-time pedestrian counts and vehicle density, assuring smooth traffic flow and pedestrian safety. Vulnerable pedestrians, such as the elderly and disabled, are accommodated by dynamically extending green light durations to ensure safe crossing. The technology also includes real-time meteorological data, such as rain, to extend green light durations and improve pedestrian safety. IntelliCross, by combining IoT sensors with machine learning, offers a scalable and cost-effective solution for improving urban traffic management, closing crucial gaps in present systems, and contributing to the development of smart cities. Public surveys demonstrate considerable support for systems that prioritize emergency vehicles while also assuring pedestrian safety, proving the system's ability to revolutionize urban traffic infrastructure.enAdaptive Traffic ManagementIoT-Bases SystemMachine LearningPedestrian SafetySmart CitiesArticleDOI: 10.1109/ICARC64760.2025.10963061