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Sensors 2019, 19(8), 1776; https://doi.org/10.3390/s19081776

A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network

1
Department of Computer Science and Automatics, University of Bielsko-Biała, ul. Willowa 2, 43-309 Bielsko-Biała, Poland
2
Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland
3
Institute of Innovative Technologies EMAG, 40-189 Katowice, Poland
*
Author to whom correspondence should be addressed.
Received: 13 March 2019 / Revised: 9 April 2019 / Accepted: 10 April 2019 / Published: 13 April 2019
(This article belongs to the Section Sensor Networks)
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Abstract

The paper introduces an artificial neural network ensemble for decentralized control of traffic signals based on data from sensor network. According to the decentralized approach, traffic signals at each intersection are controlled independently using real-time data obtained from sensor nodes installed along traffic lanes. In the proposed ensemble, a neural network, which reflects design of signalized intersection, is combined with fully connected neural networks to enable evaluation of signal group priorities. Based on the evaluated priorities, control decisions are taken about switching traffic signals. A neuroevolution strategy is used to optimize configuration of the introduced neural network ensemble. The proposed solution was compared against state-of-the-art decentralized traffic control algorithms during extensive simulation experiments. The experiments confirmed that the proposed solution provides better results in terms of reduced vehicle delay, shorter travel time, and increased average velocity of vehicles. View Full-Text
Keywords: traffic signal control; neuroevolution; sensor networks; neural network ensemble; decentralized systems; fuzzy cellular automata traffic signal control; neuroevolution; sensor networks; neural network ensemble; decentralized systems; fuzzy cellular automata
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bernas, M.; Płaczek, B.; Smyła, J. A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network. Sensors 2019, 19, 1776.

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