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The Smart Intersection pilot explores how edge computing, video detection and artificial intelligence can improve the monitoring and safety of urban road intersections. The system combines cameras, sensors and machine learning to analyse traffic interactions and detect potentially dangerous situations in real time. Data analytics tools enable cities to better understand traffic behaviour and support both immediate response and long-term planning of road infrastructure.Â
To improve safety at urban intersections by using real-time video analytics and edge-based artificial intelligence to detect risk situations and support data-driven decision-making for urban mobility management.Â
Urban road intersections represent one of the most critical points in city mobility systems. Approximately half of road accidents occur at intersections, many resulting in severe injuries or fatalities. The rapid evolution of urban mobility – including cars, pedestrians, bicycles and electric scooters – increases the complexity of these environments and creates new safety challenges for municipalities.Â
Digital technologies such as video detection, artificial intelligence and early warning systems offer new opportunities to analyse traffic interactions and identify risk situations. These tools can support both immediate operational responses and longer-term planning of infrastructure improvements.Â
However, deploying such technologies raises several technical and ethical challenges. Real-time analysis requires very low latency in data processing, which makes it difficult to rely exclusively on distant cloud infrastructure. In addition, transmitting raw video data from public spaces raises important privacy concerns.Â
The pilot addresses several challenges related to improving safety at urban intersections:Â
Urban intersections represent one of the most critical points in city mobility systems. In large metropolitan areas such as New York City, intersections concentrate complex interactions between different types of road users, including cars, pedestrians, bicycles and emerging forms of micro-mobility such as electric scooters. As urban mobility patterns continue to evolve, these interactions increase the risk of accidents and dangerous situations.Â
A significant share of road accidents occurs at intersections, many of them resulting in serious injuries or fatalities. For municipalities, improving safety at crossroads has therefore become a major priority. However, identifying risk situations and understanding the dynamics of traffic interactions at busy intersections remains challenging without advanced monitoring and analysis tools.Â
The pilot explored the use of edge computing combined with artificial intelligence to analyse traffic conditions directly at the intersection level.Â
Key technological components included:Â
The system enables initial analysis of video data to be performed locally at the intersection. This approach reduces latency and limits the transmission of raw video data while ensuring that only anonymised information is transmitted for further processing.Â
The Kentyou Eye platform provides a digital environment for monitoring the status of urban intersections through visual dashboards and analytics tools. The platform integrates data collected from video analytics and other urban data sources, enabling cities to visualise traffic conditions and analyse patterns over time.Â
The digital twin capabilities of Kentyou Eye allow the storage and analysis of historical traffic data, providing insights into how intersections evolve over time and how infrastructure changes may influence traffic behaviour. The integration of intersection monitoring within the mobility hypervisor also enables cross-analysis with other mobility data sources, supporting a broader understanding of urban mobility dynamics.Â
The pilot demonstrated the feasibility of combining video detection, edge computing and artificial intelligence to analyse traffic interactions at urban intersections in real time.Â
The system provides cities with improved visibility of traffic conditions at complex intersections and supports both real-time monitoring and long-term analysis of traffic patterns. This enables municipalities to better understand risk situations and to plan infrastructure improvements based on data-driven insights.Â
The pilot demonstrates the potential of combining edge computing, artificial intelligence and video analytics to improve safety and monitoring of urban intersections. The approach enables cities to analyse complex traffic environments while addressing key constraints related to latency and privacy.Â
Further developments focus on expanding the integration of intersection monitoring within broader urban mobility platforms and exploring additional deployments in other cities.Â
