A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network
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 instal...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/8/1776 |
| _version_ | 1797999600044343296 |
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| author | Marcin Bernas Bartłomiej Płaczek Jarosław Smyła |
| author_facet | Marcin Bernas Bartłomiej Płaczek Jarosław Smyła |
| author_sort | Marcin Bernas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T11:07:17Z |
| format | Article |
| id | doaj.art-4434bf5f88ec45298a60f77c36a0e6a8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:07:17Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4434bf5f88ec45298a60f77c36a0e6a82022-12-22T04:28:15ZengMDPI AGSensors1424-82202019-04-01198177610.3390/s19081776s19081776A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor NetworkMarcin Bernas0Bartłomiej Płaczek1Jarosław Smyła2Department of Computer Science and Automatics, University of Bielsko-Biała, ul. Willowa 2, 43-309 Bielsko-Biała, PolandInstitute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, PolandInstitute of Innovative Technologies EMAG, 40-189 Katowice, PolandThe 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.https://www.mdpi.com/1424-8220/19/8/1776traffic signal controlneuroevolutionsensor networksneural network ensembledecentralized systemsfuzzy cellular automata |
| spellingShingle | Marcin Bernas Bartłomiej Płaczek Jarosław Smyła A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network Sensors traffic signal control neuroevolution sensor networks neural network ensemble decentralized systems fuzzy cellular automata |
| title | A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network |
| title_full | A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network |
| title_fullStr | A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network |
| title_full_unstemmed | A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network |
| title_short | A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network |
| title_sort | neuroevolutionary approach to controlling traffic signals based on data from sensor network |
| topic | traffic signal control neuroevolution sensor networks neural network ensemble decentralized systems fuzzy cellular automata |
| url | https://www.mdpi.com/1424-8220/19/8/1776 |
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