Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks
The increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed in...
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MDPI AG
2024-12-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/12/12/539 |
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| author | Sergio Rojas-Blanco Alberto Cerezo-Narváez Manuel Otero-Mateo Sol Sáez-Martínez |
| author_facet | Sergio Rojas-Blanco Alberto Cerezo-Narváez Manuel Otero-Mateo Sol Sáez-Martínez |
| author_sort | Sergio Rojas-Blanco |
| collection | DOAJ |
| description | The increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed information about the relationships between all signals within a network. This list is based on stable structural road and traffic lights data and offers a crucial global perspective for signal coordination, especially in managing multiple intersections. An adjacency list is more efficient than matrices in terms of space and computational cost, allowing for the identification of critical signals before applying advanced optimization techniques such as neural networks or hypergraphs. We successfully tested the proposed method on three networks of varying complexity extracted from VISSIM and VISUM, demonstrating its effectiveness even in networks with up to 8372 links and 547 traffic lights. This tool provides a solid foundation for improving urban traffic management and coordinating signals across intersections. |
| format | Article |
| id | doaj-art-eb3e685856fa479b928f7e44148d9150 |
| institution | DOAJ |
| issn | 2079-8954 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-eb3e685856fa479b928f7e44148d91502025-08-20T02:56:55ZengMDPI AGSystems2079-89542024-12-01121253910.3390/systems12120539Adjacency List Algorithm for Traffic Light Control Systems in Urban NetworksSergio Rojas-Blanco0Alberto Cerezo-Narváez1Manuel Otero-Mateo2Sol Sáez-Martínez3Department of Mechanical Engineering and Industrial Design, Universidad de Cádiz, Avda, Universidad de Cádiz no 10, Puerto Real, 11519 Cádiz, SpainDepartment of Mechanical Engineering and Industrial Design, Universidad de Cádiz, Avda, Universidad de Cádiz no 10, Puerto Real, 11519 Cádiz, SpainDepartment of Mechanical Engineering and Industrial Design, Universidad de Cádiz, Avda, Universidad de Cádiz no 10, Puerto Real, 11519 Cádiz, SpainDepartment of Mathematics, Universidad de Cádiz, Avda, Universidad de Cádiz no 10, Puerto Real, 11519 Cádiz, SpainThe increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed information about the relationships between all signals within a network. This list is based on stable structural road and traffic lights data and offers a crucial global perspective for signal coordination, especially in managing multiple intersections. An adjacency list is more efficient than matrices in terms of space and computational cost, allowing for the identification of critical signals before applying advanced optimization techniques such as neural networks or hypergraphs. We successfully tested the proposed method on three networks of varying complexity extracted from VISSIM and VISUM, demonstrating its effectiveness even in networks with up to 8372 links and 547 traffic lights. This tool provides a solid foundation for improving urban traffic management and coordinating signals across intersections.https://www.mdpi.com/2079-8954/12/12/539urban road networktraffic light controlintelligent transportation systemtraffic predictionadjacency matrix |
| spellingShingle | Sergio Rojas-Blanco Alberto Cerezo-Narváez Manuel Otero-Mateo Sol Sáez-Martínez Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks Systems urban road network traffic light control intelligent transportation system traffic prediction adjacency matrix |
| title | Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks |
| title_full | Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks |
| title_fullStr | Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks |
| title_full_unstemmed | Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks |
| title_short | Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks |
| title_sort | adjacency list algorithm for traffic light control systems in urban networks |
| topic | urban road network traffic light control intelligent transportation system traffic prediction adjacency matrix |
| url | https://www.mdpi.com/2079-8954/12/12/539 |
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