Traffic Light Optimization with GANZ PixelPro AI

Case Study

Client: Municipality in Southern Italy

Application:

• Traffic light optimization
• Traffic control
• Statistical data collection

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Client Request / Challenge:

The client required the implementation of an AI-based video analysis system to optimize traffic light regulation in areas experiencing heavy traffic. The primary goals for the installation included:

• Dynamic adjustment of traffic lights based on real-time traffic volume.
• Classification of transiting vehicles by category: cars, trucks, buses, motorcycles, and bicycles.
• Collection of statistical data for in-depth traffic analysis and potential integration with broader urban management system

Technical Solution:

To meet the client's requirements, GANZ PixelPro AI cameras were selected for installation. This camera type features integrated advanced video analytics. Through an additional AI license, the cameras can identify and classify the different vehicle categories.

Cameras were strategically positioned along key arterial roads to analyze traffic volume in real-time, detecting both the presence and category of vehicles. When congestion or a significant number of vehicles is detected, the system automatically adjusts the green light duration to optimize traffic flow.

Project Development & Implementation:

GANZ provided its extensive security sector experience to support both the client and the installing company. The analysis phase determined that a system with integrated analytics was the optimal approach.

A total of 128 GANZ PixelPro AI cameras were installed along the main roads. This placement ensures effective monitoring under various traffic conditions. Additionally, the activation of the extra AI license has enabled the advanced vehicle classification features required for the project.

Results & Benefits:

The application of GANZ AI technology provided an effective solution for urban traffic management, delivering tangible benefits in efficiency and cost:

• Optimized Traffic Flow: Reduced waiting times and improved road fluidity, particularly during peak hours.
• Cost Containment: Utilizing cameras with integrated AI video analytics eliminated the need for expensive centralized infrastructure, making the system feasible within a competitive budget.
• Advanced Data Collection: The data gathered on vehicle categories and traffic volumes provides valuable support for future traffic studies and the development of urban mobility strategies.
• Scalability: The system is easily adaptable and can be implemented in other urban areas. It also offers the potential to integrate additional functionalities based on future client needs.
• Smart City Advancement: The project represents a significant step towards adopting smart city technologies, enhancing the citizen experience and providing valuable tools for more sustainable urban management.

Future Potential:

The system has the potential for future integration into a centralized network. This would allow for remote monitoring and real-time reporting of traffic anomalies or incedents.