AIGLE is an AI-powered traffic detection and vehicle counting system developed for the Department of Transportation (Dishub) of East Java Province. The platform leverages computer vision to provide real-time traffic monitoring and data-driven insights for urban transportation planning.
Key Features
1. Real-Time Vehicle Detection
Uses deep learning-based object detection models to identify and classify vehicles from live CCTV feeds. The system distinguishes between motorcycles, cars, buses, and trucks, providing granular traffic composition data at each monitored intersection.
2. Automated Traffic Counting
Continuously counts vehicles passing through designated monitoring zones. Automated counting eliminates the need for manual surveys, delivering consistent and accurate volume data around the clock.
3. Interactive Dashboard
A web-based dashboard presents traffic volume trends, peak hour analysis, and historical comparisons through interactive charts and maps. Operators can filter data by location, vehicle type, and time range to quickly identify congestion patterns.
4. Multi-Camera Management
Supports simultaneous feeds from multiple CCTV cameras deployed across East Java's road network. Administrators can add, configure, and monitor camera streams from a centralized interface.
5. Data Export and Reporting
Generates downloadable reports in common formats for use in policy documents, infrastructure planning, and inter-agency coordination. Aggregated data helps officials make evidence-based decisions on road improvements and signal timing.
Purpose and Benefits
AIGLE transforms raw CCTV footage into actionable traffic intelligence. By automating vehicle detection and counting, the system reduces reliance on manual observation, lowers operational costs, and provides Dishub with the continuous data needed to manage East Java's growing transportation demands effectively.