Rosell Rye B. Coronacion, Jose Enrique L. Lopez, Amgelo Gio P. Luminoque, Jezreel Aron L. Sican, John Gale M. Viray.  4  0   <br/><br/>
Adaptive traffic light system utilizing artificial neural network.        6 
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<br/><br/>   Undergraduate Thesis : (bachelor of Science in Electronics Engineering) - Pamantasan ng Lungsod ng Maynila, 2024. 
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<br/><br/> ABSTRACT:  In the Philippines, the majority of the traffic lights systems that are utilized are still the traditional traffic lights system which is a signal on a fixed timer basis. As a result, the traditional traffic lights cannot manage to deal with the unexpected changes in traffic volume especially during peak hours and ultimately results in traffic congestion. A new AdaptiveTraffic Light System is also being implemented by the Metropolitan Manila Development Authority (MMDA) in some areas in Metro Manila to serve as an additional measure to mitigate the traffic congestion. However, this new Adaptive Traffic Lights System disregards the timer function of the traffic lights system. This study presents the development of Adaptive Traffic Light System utilizing Artificial Neural Network. This research developed a Traffic Tracking Module integrated with an Artificial Neural Network (ANN) that can predict the optimal signal timings based on real-time road conditions. The integration of the ANN into the Traffic Tracking Module demonstrated a 76% success rate in predicting optimal timing during both normal and peak traffic hours, showing high adaptability to different conditions.       
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