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A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems
V.V. Myasnikov 1,2, A.A. Agafonov 1, A.S. Yumaganov 1

Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34,
IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151

 PDF, 958 kB

DOI: 10.18287/2412-6179-CO-1031

Pages: 917-925.

Full text of article: Russian language.

In this paper, we propose a traffic signal control method in intelligent transportation and geoinformation systems, based on a deterministic predictive model. The method provides adaptive control based on traffic data, including data from connected and autonomous vehicles. The proposed method is compared with the state-of-the-art traffic signal control solutions: empirical control algorithms and reinforcement learning-based control methods. An advantage of the proposed method is shown and directions of further research are outlined.

image segmentation, road pavement distress, synthetic dataset, generative adversarial network, convolutional neural network.

Myasnikov VV, Agafonov AA, Yumaganov AS. A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems. Computer Optics 2021; 45(6): 917-925. DOI: 10.18287/2412-6179-CO-1031.

The work was supported by the Russian Science Foundation under grant No.21-11-00321, https://rscf.ru/en/project/21-11-00321/.


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