An Adaptive Traffic Light Control System Based On Artificial Intelligence
DOI:
https://doi.org/10.32792/jeps.v14i1.389Abstract
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.
References
[ 1] Bhosale, P., Kawatikawar, A., Jadhav, P., & Patil, S. (2022). Vehicle Traffic Analysis using CNN Algorithm.
[ 2] Jensen, M. B., Bahnsen, C. H., Lahrmann, H. S., Madsen, T. K. O., & Moeslund, T. B. (2018). Collecting traffic video data using portable poles: survey, proposal, and analysis. Journal of Transportation Technologies, 8(4), 88175.
[ 3] Gebregeorgis, B. M., & Sarmah, D. K. (2016). Smart traffic light controlling and violation detection system using digital image processing. International Journal of Engineering Research, 4(2), 520-529.
[ 4] Xie, X. F., Smith, S., & Barlow, G. (2012). Schedule-driven coordination for real-time traffic network control. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 22, pp. 323-331).
[ 5] Sharma, M., Bansal, A., Kashyap, V., Goyal, P., & Sheikh, T. H. (2021). Intelligent traffic light control system based on traffic environment using deep learning. Materials Science and Engineering (Vol. 1022, No. 1, p. 012122).
[ 6] Chandrasekara, W. A. C. J. K., Rathnayaka, R. M. K. T., & Chathuranga, L. L. G. (2020, December). A real-time density-based traffic signal control system. In 2020 5th International Conference on Information Technology Research (ICITR) (pp. 1-6).
[ 7] Zhang, R., Ishikawa, A., Wang, W., Striner, B., & Tonguz, O. K. (2020). Using reinforcement learning with partial vehicle detection for intelligent traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 22(1).
[ 8] Natafgi, M. B., Osman, M., Haidar, A. S., & Hamandi, L. (2018, November). Smart traffic light system using machine learning. IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) (pp. 1-6). IEEE.
[ 9] Navarro-Espinoza, A., López-Bonilla, O. R., García-Guerrero, E. E., Tlelo-Cuautle, E., López- Mancilla, D., Hernández-Mejía, C., & Inzunza-González, E. (2022). Traffic flow prediction for smart traffic lights using machine learning algorithms. Technologies, 10(1).
[ 10] Saleem, M., Abbas, S., Ghazal, T. M., Khan, M. A., Sahawneh, N., & Ahmad, M. (2022). Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal, 23(3).
[ 11] Aleko, D. R., & Djahel, S. (2020). An efficient adaptive traffic light control system for urban road traffic congestion reduction in smart cities. Information, 11(2).
[ 12] Niu, C., & Li, K. (2022). Traffic light detection and recognition method based on YOLOv5s and AlexNet. Applied Sciences, 12(21).
[ 13] Kim, P. (2017). Matlab deep learning. With machine learning, neural networks and artificial intelligence, 130(21).
[ 14] Tammina, S. (2019). Transfer learning using vgg-16 with deep convolutional neural network for classifying images. International Journal of Scientific and Research Publications (IJSRP), 9(10), 143-150.
[ 15] Sewak, M., Karim, M. R., & Pujari, P. (2018). Practical convolutional neural networks: implement advanced deep learning models using Python. Packt Publishing Ltd.
[ 16] Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., & Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.
[ 17] Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
[ 18] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
[ 19] Muhammad, N. A., Nasir, A. A., Ibrahim, Z., & Sabri, N. (2018). Evaluation of CNN, alexnet and GoogleNet for fruit recognition. Indonesian Journal of Electrical Engineering and Computer Science, 12(2).
[ 20] Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan kaufmann.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Education for Pure Science- University of Thi-Qar
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Policy
Authors retain copyright of their articles published in the Journal of Education for Pure Science (JEPS).
By submitting their work, authors grant the journal a non-exclusive license to publish, distribute, and archive the article in all formats and media.
License
All articles published in JEPS are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided that the original author(s) and the source are properly credited.
Author Rights
Authors have the right to:
-
Share their articles on personal websites, institutional repositories, and academic platforms
-
Reuse their work in future research and publications
-
Distribute the published version without restriction
Journal Rights
The journal retains the right to:
-
Publish and archive the articles
-
Include them in indexing and archiving systems such as LOCKSS and CLOCKSS
-
Promote and disseminate the published work
Responsibility
The contents of all articles are the sole responsibility of the authors. The journal, editors, and editorial board are not responsible for any errors, opinions, or statements expressed in the published articles.
Open Access Statement
JEPS provides immediate open access to its content, supporting the principle that making research freely available to the public enhances global knowledge exchange.
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0/
