Frequently Asked Questions

Our system uses AI-powered cameras and IoT sensors to monitor traffic patterns in real-time. Machine learning algorithms analyze this data to optimize traffic light timing, predict congestion, and provide intelligent routing suggestions. The system continuously learns and adapts to improve traffic flow.

As a student-led project, we're currently in the pilot phase. For a small intersection, initial setup takes 2-4 weeks including sensor installation and system calibration. City-wide deployment would be phased over 6-12 months depending on infrastructure complexity.

We're developing cost-effective solutions suitable for various budgets. Initial pilot programs focus on proving the technology's value. Costs vary based on intersection complexity, existing infrastructure, and required sensors. We provide detailed proposals after site assessment.

Data security is paramount. We use encrypted data transmission, secure cloud storage, and follow privacy regulations. All personal vehicle data is anonymized. Only traffic pattern data is processed to optimize signal timing while protecting individual privacy.

Yes! Our system is designed to integrate with most existing traffic infrastructure. We work with current signal controllers and gradually enhance capabilities. The modular approach allows cities to upgrade incrementally without complete system replacement.

Our simulations show 25-40% reduction in idle time at intersections, leading to decreased emissions. Optimized traffic flow reduces fuel consumption and air pollution. Better traffic management contributes to more sustainable urban transportation.

The AI system includes emergency vehicle detection and priority protocols. When emergency vehicles approach, the system automatically adjusts signal timing to create clear corridors. Manual override capabilities ensure traffic managers maintain control during critical situations.

Contact our team for an initial consultation and site assessment. We'll analyze your current traffic patterns, identify improvement opportunities, and propose a pilot program. As students, we're eager to collaborate and demonstrate our technology's potential.