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As part of the EIT Urban Mobility funded AI4LIFE project, UTA Member Kentyou, the City of Istanbul and ISBAK (the municipal company of Istanbul) deployed an AI-based smart intersection management system. The system integrates IoT infrastructure and AI-driven data analytics to improve traffic flow, reduce congestion and enhance road safety at urban intersections.
Key goal:
To reduce traffic congestion and accidents in urban areas through an integrated AI-based intersection management system, ultimately improving traffic flow, safety and quality of life for citizens.
Cities are responsible for approximately 70% of global greenhouse gas emissions, with road transportation accounting for about a quarter of these emissions. Traffic congestion alone costs Europe €110 billion annually and contributes significantly to air pollution-related health issues. In addition, 20 000 people die annually in road accidents across Europe, with 40% of fatalities occurring in cities and 50% at intersections.
Intersections play a critical environmental, economic and social role in urban systems. At the same time, they are key bottlenecks in city infrastructure and hotspots for traffic accidents.
The AI4LIFE project builds on Kentyou’s prior collaboration with Columbia University, where a proof of concept was developed for a single intersection. This work demonstrated the potential of AI-driven intersection management and was recognised in IDC Government Insight’s 2023 Smart Cities North America Awards in the transportation category.
City-specific challenge
Improve traffic flow and safety at intersections while enhancing user experience for citizens and mobility management operators.
• Deployment of an integrated AI-based smart intersection management system
• Integration of ISBAK’s IoT infrastructure and adaptive signalised intersection management system
• Integration of Kentyou’s interoperable AI-driven data platform
• Development of visualisation and decision-support tools
• Automation of abnormal traffic situation detection
• Consolidation of multiple traffic monitoring functionalities into a single main tool
The integrated solution combines IoT devices with a software tool enabling data-driven decision making for sustainable mobility.
The pilot validated the integration of IoT infrastructure and AI-driven data analytics into a unified intersection management system. It demonstrated automated detection of abnormal traffic situations and real-time support to traffic operators. The system consolidated traffic monitoring, data analysis, camera surveillance, event tracking and impact monitoring functionalities into a single interface.
The project reduced the time required to detect abnormal traffic situations and initiate interventions. Instead of relying primarily on manual monitoring of cameras and traffic intensity maps or citizen calls, operators received automated alerts highlighting potential issues. The system streamlined traffic management workflows by reducing the need to operate multiple tools simultaneously.
In the short term, the project improved operational efficiency within traffic control centres by automating detection processes and centralising traffic management functionalities. The solution supports improved traffic flow management and enhanced road safety at intersections.
The project demonstrated the importance of integrating IoT infrastructure with interoperable data platforms to enable automated traffic monitoring. It also confirmed the value of consolidating monitoring and decision-support tools into a unified system for traffic operators.
The AI4LIFE project provided a practical demonstration of AI-based smart intersection management in a complex metropolitan environment. The pilot in Istanbul validated the integrated approach combining IoT infrastructure and AI-driven analytics to address congestion and safety challenges at intersections.
The project includes development of a commercialisation strategy to scale the solution in Europe and beyond. The integrated system is intended for further deployment in urban environments seeking to improve traffic flow and safety through AI-based intersection management.
