A Smart Traffic Management System Powered by Artificial Intelligence Designed to Optimize Traffic Light Timings in Real-Time for Congested Areas.
Mr. Arun Kumar K, Assistant professor
Department of Information Technology Sathyabama Institute of Science and Technology
Chennai, India
arunkumar.k.it@sathyabama.ac.in
Aswin S UG Student
Department of Information Technology Sathyabama Institute of Science and Technology
Chennai, India
aswinkpkm@gmail.com
Christo Sham K,
UG Student Department of Information Technology
Sathyabama Institute of Science and Technology
Chennai, India Christosham05@gmail.com
Ms. L. Mary Gladence, Professor
Department of Information Technology Sathyabama Institute of Science and
Technology Chennai, India
Marygladence.it@sathyabama.ac.in
Abstract— The proposed development plan is for an AI-assisted smart traffic control system. This system would employ live camera images from traffic surveillance cameras installed in cities to monitor, assess, and optimize traffic flow at the city's traffic junctions. OpenCV technology would be employed in this project to constantly assess live video taken from traffic cameras installed at cities to detect cars, ascertain the level of congestion, observe patterns of traffic, and gauge traffic jams across every traffic intersection in real time. The system utilizes smart image analysis and machine learning algorithms to evaluate traffic flow from all directions and then optimizes traffic signal timings accordingly in an instant. Unlike traditional fixed-time traffic signals, which follow an installed schedule, this auto-learning system would instantly react accordingly to prevailing conditions, allocate relatively equal green time to congested routes, and decrease idling time at lesser-congested routes. The system would work continuously, learning and adjusting accordingly according to prevailing rush hours, accidents, events, or weather. Its positive impact would include reducing traffic jams, fuel, and emissions, and idling time as well, and make roads much safer. It can be easily scaled up and installed at all city traffic crossings with ease.
Keywords:
AI-enabled smart traffic management, real-time traffic monitoring, computer vision, OpenCV, live camera feeds, traffic density estimation, vehicle detection, congestion analysis, adaptive traffic signal control, dynamic light timing optimization,
