AI-DRIVEN DYNAMIC TRAFFIC SIGNAL SYSTEM USING YOLO FOR REAL TIME TRAFFIC OPTIMIZATION

Abstract

The AI-D riven Dynamic Traffic Signal System using YOLO for Real-Time Traffic Optimization that leverages the YOLO (You Only Look Once) model for real-time dynamic signaling and traffic optimization. By integrating a microcontroller with a CCTV camera, the system continuously monitors traffic conditions and intelligently adjusts traffic signal timings based on detected vehicle density at intersections. The YOLO model processes the video feed to count vehicles, allowing the system to dynamically modify green signal durations to optimize traffic flow. To maintain balance across all directions, it sets a maximum green light duration, preventing excessive delays in opposing traffic. This innovative approach enhances traffic management, reduces congestion, and contributes to improved road safety, showcasing the transformalive potential of AI technologies in urban traffic systems.

Specification

DESCRIPTION
The AI-Driven Dynamic Traffic Signal System using YOLO for Real Time Traffic
Optimization is an effective way to optimize the traffic in urban areas where there is high
traffic congestion. By incorporating this system, the waiting time and fuel consumption is .
drastically reduced, ensuring the steady flow of traffic wiihout stagnation. The YOLO
mechanism ensures efficient and a reliable usage in urban areas by regulating the traffic
flow.
BACKGROUND ART
In recent years, the significant increase in private vehicie usage, driven -by rising
population and urban expansion, has intensified the challenges of road transportation. As
roadways have become the backbone of daily commuting for most people, efficient traffic
management has become crucial to prevent delays, reduce fuel consumption, and lower
vehicular emissions. However, traditional traffic signal syslerns, with their fixed, pre-programmed cycles, fail to adjust to real-time traffic conditions. This often results in one
t1l Ill direction remaining congested while other directions experience minimal traffic,
To address these issues, we propose a dynamic traffic signal control system powered by
an Al-based YOLO (You Only Look Once) model. This system will leverage YOLO's
object detection capabilities to assess traffic density in each direction and dynamically
adjust the signal timings, optimizing the flow of vehicles in real-time.
The system will utilize Raspberry Pi microcontrollers due to their affordability, energy
efficiency, and versatility, making them ideal for prototyping and deployment in urban
environments. Additionally, CCTV cameras installed at signal posts will provide
continuous real-time monitoring and deliver high-quality footage for the YOLO model to
process. The integration of these technologies will ensure an adaptive and intelligent
traffic signal system that reduces congestion, minimizes fuel consumption, a~d lowers
emissions, creating a more efficient and sustainable urban transportation network .
NOVEL SYSTEM AND METHOD:
The AI-Driven Dynamic Traffic Signal System using YOLO for Real-Time Traffic
Optimization operates utilizing the YOLO (You Only Look Once) model, renowned for its
precise and efficient object detection capabilities. The system comprises a microcontroller
to manage signal operations and a CCTV camera for continuous real-time monitoring of
traffic. The YOLO model processes the video feed from the CCTV camera, detecting
vehicles and calculating the traffic density at each intersection.
Based on the detected vehicle count, the system dynamically adjusts the green signal
timing (flow time) for each direction. When a direction is in the waiting phase (red signal),
the system determines how long the green signal should last depending on the traffic
volume. To ensure balanced traffic flow, the system sets a maximum flow time to prevent
excessive green light in one direction, which could otherwise cause extended waiting times
in other directions. This adaptive approach optimizes traffic management, minimizes
- delays, and ensures smooth traffic movement across all directions at the intersection.
COMPONENTS:
• Raspberry Pi
• L_EDs [for simulating signal lights]
• Resistors
• CCTV Cameras
• Breadboard
• Connecting jumper wires
• Power supply
CLAIMS
We Claim,
1. The system according to claim 1, incorporates a YOLO algorithm that dynamically
adjusts traffic signal timings based on real-time traffic conditions, significantly
improving upon traditional fixed-timing systems that were preprogrammed.
2. This system according to claim 2, integrates Al-based decision-making that operates
without human intervention, offering a fully automated solution for managing traffic
signals and reducing the need for manual adjustments.
3. This system according to daim 3, reduces fuel consumption and emissions by
minimizing vehicle idle time through adaptive trafFic control, a notable improvement
over traditional system.
4. This· system according to claim 4, provides real-time traffic flow adjustments,
responding dynamically to sudden traffic changes caused by events or accidents,
preventing congestion buildup.
5. The traffic signal system works autonomously to detect and manage congestion.
preventing traffic surges, as stated in claim 4, making it more efficient in high-traffic areas.
6. This system according to claim 5, leverages loT and AI technologies to provide realtime
monitoring, traffic con~rol, and future integration into smart city infrastructure.
7. According to claim 6, the system is scalable and can be implemented across multiple
intersections with minimal maintenance, offering flexibility that traditional systems lack.

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