E3S Web Conf.
Volume 217, 2020International Scientific and Practical Conference “Environmental Risks and Safety in Mechanical Engineering” (ERSME-2020)
|Number of page(s)||12|
|Section||Transport Energy Efficiency and Smart Mobility|
|Published online||14 December 2020|
Development of intelligent system for automated traffic control
Don state technical University, Rostov–on–Don, Gagarin square, 1, Russia
* Corresponding author: email@example.com
This article is devoted to the issue of regulating traffic congestion in major cities of the world using artificial neural networks. Research is aimed at developing import – substituting automated intelligent system that uses artificial neural network to make decisions to optimize traffic congestion by changing the duration of light phases of traffic lights. Multilayer perceptron with sigmoidal activation function is used as neural network. The article describes developing stages of intelligent automated traffic control system that using artificial neural networks allows making informed decisions based on extensive analysis of available information, as well as constantly adapt it to incoming external influences that lead to non – equilibrium state. Practical application of the proposed system is expressed in unloading road sections adjacent to highway; reducing the number of traffic jams in the lanes or reducing the length of the car queue; automating traffic control and reducing the number of emergency cases that require inspection personnel to leave for manual control. System allow improving overall traffic situation by avoiding cascading traffic jams on adjacent sections; prevention of accidents and conflicts between motorists and pedestrians; improving the reliability of adjustment and reducing cost of maintenance infrastructure.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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