Traffic congestion in road transportation presents a significant issue for both developed and
developing nations. This research leverages artificial intelligence (AI) to assess, retime, and analyze
pre-timed traffic signals in the Oredo local government area of Edo State, where vehicle volume has
recently surged. Traffic data, including vehicle flow, composition, and movement, was manually
recorded at 15-minute intervals. Machine learning techniques, particularly an Artificial Neural
Network (ANN) combined with SUMO (Simulation of Urban Mobility), were applied to optimize signal
timing and reduce intersection control delay. The analysis demonstrated a 15% reduction in average
control delay, from 44.4 seconds to 36.6 seconds, and improved the level of service (LOS) from 'D' to
'C' during peak hours. The ANN model achieved a low root mean squared error (RMSE) of 2.26 × 10^-
13, indicating high accuracy. The optimal model, which featured four hidden layers with 64 neurons
each and was trained for 250 epochs, achieved the best validation performance of 0.01748667. These
results demonstrate the effectiveness of AI-based models in improving traffic signal performance and
mitigating congestion at intersections.