AI Traffic Control System
To address the growing problem of urban traffic congestion, I developed a concept for an AI-driven traffic management system that integrates computer vision, IoT sensors, and reinforcement learning to optimize traffic light control in real time. Traditional signal systems rely on fixed timers that cannot adapt to dynamic road conditions, resulting in unnecessary delays, fuel waste, and increased emissions. This project applied artificial intelligence to make traffic flow adaptive, efficient, and responsive to emergencies.
The system’s architecture centers on a Raspberry Pi microcontroller, which acts as the central processing unit. Two mounted cameras capture live video feeds from both directions of an intersection. Using image processing and density estimation algorithms, the system counts vehicles and determines lane occupancy levels. Based on these values, the Raspberry Pi dynamically adjusts the green light duration for each lane to match real-time traffic density.
To handle emergency vehicle scenarios, the design includes specialized detection mechanisms such as sound recognition for sirens or visual identification of flashing red-and-blue lights. When an emergency vehicle is detected, the control logic overrides the regular signal pattern and immediately grants a green light in that direction, while holding red for other lanes to ensure a clear passage.
The traffic signal timing and logic are managed through a combination of Python-based control algorithms and reinforcement learning techniques, allowing the system to learn optimal signal strategies through repeated simulation. The reinforcement learning model rewards reduced waiting time, balanced throughput, and quick emergency response, enabling continuous improvement over time.
Testing and simulation were carried out using Proteus and SUMO (Simulation of Urban Mobility) to model real-world traffic scenarios. Results showed improved intersection flow, reduced idle time, and significant gains in emergency response efficiency. The design also supports data logging through the Raspberry Pi’s connection to a computer, allowing for long-term traffic pattern analysis.
