ITDS | SPMS

INTELLIGENT TRAFFIC LIGHT: OPTIMIZING SIGNAL TIMING FOR EFFICIENCY AND REDUCED CONGESTION

Project Description
Supervisor

Traffic light system has significantly contributed to the ease and convenience of modern transportation and traffic management. It has significantly reduced the occurrence of road accidents, and travel time. Ghana is used to the static time based traffic light for several years, since the installation of the first traffic light from the 1900’s to the 2000’s. With the rampant rise of modern technology and Artificial Intelligence, traffic lights can be upgraded to make smart logical decision. One of these decisions is the use of Artificial Intelligence to decongest lanes intelligently. A road with most congestion is allowed to move first, while saving time. In other words, for a four way junction where a lane has green light on although the are no or few cars on that lane, AI can be applied to tur that road red and chose to decongest from the remaining lanes, which lane has the most congestion. This logical decision can be repeated to all traffic lights in the nation to curb unnecessary time wasting and increase road traffic efficiency and productivity. This project has the objective of building an Intelligent Traffic Light System that will make logical decisions to decongest and save time of road travels in Ghana.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

Intelligent traffic light: optimizing signal timing for efficiency and reduced congestion

2024/2025

Feb 12, 2026

UEB1106821- NKRUMAH TABIRI KWAME, UWB3203623- ADU FELIX, UWB3200921- YIDANA SALIFU MOHAMMED, UWB3202122- BOATENG KINGSFORD, UWB3200622- AWASABISAH ROLAND

BSc Information Technology