An Adaptive  Traffic Light Control System Based On Artificial Intelligence

Hind  Ali; D. Mustafa  J. Hayawi

doi:10.32792/jeps.v14i1.389

المؤلفون

Hind Ali
D. Mustafa J. Hayawi

DOI:

https://doi.org/10.32792/jeps.v14i1.389

الملخص

Abstract:
The phenomenon of traffic congestion is one of the most widespread phenomena in the world, especially in
developed countries where the number of vehicles is increasing, which means that many traffic jams occur in the
streets, especially at times when employees go to and from work, which is known as peak times and is
concentrated usually in the early morning, and this gives a great feeling of annoyance and boredom as a result of
wasting hours of time in the streets, and being late for work because of not being able to reach it at the appropriate
time. Add to that a signal system traditional traffic, which played a prominent role in these bottlenecks, can no
longer smooth management and organization of traffic, because it did not develop with the development of cities.
From this point of view, studied and applied, a system based on image processing that uses the technology to detect
objects in the image by removing impurities, and thus determining the traffic density based on the number of
objects. In the picture, which represents the vehicles, the traffic lights were controlled depending on the traffic
density on each road. The proposed system is implemented using MATLAB R2021 language, and for performance
evaluation, classification accuracy is used as the evaluation metric. On traffic lots (MTID) the data set is frames of
video recorded at 40000 (frames per second) FPS and taken by a drone. The Multi-view Traffic Intersection
Dataset is used in the proposal system, it is available on "https://www.kaggle.com/datasets" . Categories: Bicycles,
Cars, Buses, Trucks. To test the effectiveness of the proposed approach. Three pre-trained networks (AlexNet,
ResNet, and DenseNet) were implemented. Using the ResNet50 model resulted in a significant improvement in
performance. In particular, the proposed system using ResNet50 achieves an accuracy average of 99.47% on the
(MTID) datasets respectively.

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