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Adaptive traffic light system utilizing artificial neural network. 6

By: Rosell Rye B. Coronacion, Jose Enrique L. Lopez, Amgelo Gio P. Luminoque, Jezreel Aron L. Sican, John Gale M. Viray. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 4541346Edition: Description: Content type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

Contents:

Action note: In: Summary: 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. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	TK7882 .C67 2024 (Browse shelf)	Available		FT7908

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Undergraduate Thesis : (bachelor of Science in Electronics Engineering) - Pamantasan ng Lungsod ng Maynila, 2024. 56

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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