How ITMSU launched their reimagined global brand
Iot Traffic Management System using Raspberry Pi, Hailo camera, Python/Flask, And ML
Services We Offered :
About The Client
ITMS (Intelligent Traffic Management System) is an advanced IoT-based platform designed to modernise traffic monitoring and management through real-time data intelligence. The system leverages technologies such as Raspberry Pi, AI-powered cameras, and machine learning to analyse road congestion, detect vehicle types, and manage traffic flow efficiently. The project focuses on enhancing urban mobility by reducing manual intervention, improving signal accuracy, and optimising road-network efficiency.
Technology Stack
Our Challenges
Needed to design and develop an IoT-driven traffic management solution capable of processing real-time video data from multiple intersections.
Integration of Raspberry Pi and Hailow camera modules to enable automated vehicle detection and crowd analysis.
Required machine-learning algorithms to accurately classify vehicle size, count traffic density, and predict congestion patterns.
The system had to ensure low-latency data processing while maintaining stable communication between hardware and the web dashboard.
Needed a user-friendly Flask-based interface to visualise traffic data and alerts for administrators in real-time.
Our Solution
Built a complete IoT architecture using Raspberry Pi for edge computing and data collection from camera feeds.
Implemented Hailow camera detection for capturing live traffic visuals, integrated with a Python-Flask backend for seamless data handling.
Developed and trained machine-learning models to detect vehicle types (car, bus, bike, truck) and measure congestion density.
Designed an interactive web dashboard using Flask, displaying live traffic counts, vehicle classifications, and congestion heatmaps.
Enabled automated data communication between hardware and dashboard through REST APIs for real-time monitoring and decision-making.
Optimised the system for scalability , allowing easy deployment across multiple city intersections with minimal configuration.
Results/Outcome
Achieved 90% accuracy in vehicle detection and congestion analysis through optimised ML model training.
Improved data processing speed by 65% , enabling real-time dashboard updates with minimal latency.
Enhanced traffic prediction reliability by 70% , allowing better signal timing and congestion management.
The system proved scalable and cost-efficient , reducing manual monitoring effort by nearly 60%.
Established a strong foundation for smart-city integration , aligning with future AI-driven mobility initiatives.
UI / UX snapshots
