Застосування алгоритму оптимізації мурашиної колонії в світлофорному керуванні
| dc.citation.epage | 34 | |
| dc.citation.issue | 2 | |
| dc.citation.journalTitle | Комп'ютерні системи та мережі | |
| dc.citation.spage | 26 | |
| dc.citation.volume | 6 | |
| dc.contributor.affiliation | Національний університет “Львівська політехніка” | |
| dc.contributor.affiliation | Lviv Polytechnic National University | |
| dc.contributor.author | Данилюк, А. Г. | |
| dc.contributor.author | Danyliuk, A. | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2025-12-11T11:15:27Z | |
| dc.date.created | 2024-10-10 | |
| dc.date.issued | 2024-10-10 | |
| dc.description.abstract | Актуальність дослідження зумовлена необхідністю оптимізації світлофорного контролю перехресть з метою зменшення кількості заторів і затримок та збільшенням пропускної здатності перехресть. Ефективним рішенням цієї проблеми є використання інтелектуальних транспортних систем та окремих підсистем прийняття рішень. Однак автоматизація таких завдань потребує наукових досліджень для отримання ефективних алгоритмів, придатних для використання на практиці. Мета роботи полягає у пропозиції підходу до оптимізації світлофорного контролю перехресть, що враховує не лише параметри транспортного потоку на одному конкретному перехресті, а й параметри транспортного потоку на суміжних перехрестях та використовує алгоритм оптимізації мурашиної колонії для оптимізації світлофорного контролю суміжних перехресть. Отримані результати показали, що використання такого підходу є більш ефективним порівняно з існуючими і має потенціал зменшити кількість затримок на 10% та збільшити пропускну здатність перехресть на 15% і більше. | |
| dc.description.abstract | The relevance of the research is determined by the need to optimize traffic light control at intersections to reduce congestion and delays and increase the capacity of intersections. A practical solution to this problem is using intelligent transport systems and specific decision-making subsystems. However, automating such tasks requires scientific research to develop effective algorithms suitable for practical use. This work proposes an approach to optimizing traffic light control at intersections that considers the traffic flow parameters at a specific intersection and those at adjacent intersections, utilizing an ant colony optimization algorithm to optimize traffic light control at neighboring intersections. The results obtained show that this approach is more effective compared to existing methods and has the potential to reduce delays by 10% and increase intersection capacity by 15% and more. | |
| dc.format.extent | 26-34 | |
| dc.format.pages | 9 | |
| dc.identifier.citation | Данилюк А. Г. Застосування алгоритму оптимізації мурашиної колонії в світлофорному керуванні / А. Г. Данилюк // Комп'ютерні системи та мережі. — Львів : Видавництво Львівської політехніки, 2024. — Том 6. — № 2. — С. 26–34. | |
| dc.identifier.citation2015 | Данилюк А. Г. Застосування алгоритму оптимізації мурашиної колонії в світлофорному керуванні // Комп'ютерні системи та мережі, Львів. 2024. Том 6. № 2. С. 26–34. | |
| dc.identifier.citationenAPA | Danyliuk, A. (2024). Zastosuvannia alhorytmu optymizatsii murashynoi kolonii v svitlofornomu keruvanni [Application of ant colony optimization algorithm in road traffic control]. Computer Systems and Networks, 6(2), 26-34. Lviv Politechnic Publishing House. [in Ukrainian]. | |
| dc.identifier.citationenCHICAGO | Danyliuk A. (2024) Zastosuvannia alhorytmu optymizatsii murashynoi kolonii v svitlofornomu keruvanni [Application of ant colony optimization algorithm in road traffic control]. Computer Systems and Networks (Lviv), vol. 6, no 2, pp. 26-34 [in Ukrainian]. | |
| dc.identifier.doi | DOI: https://doi.org/10.23939/csn2024.02.026 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/123990 | |
| dc.language.iso | uk | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Комп'ютерні системи та мережі, 2 (6), 2024 | |
| dc.relation.ispartof | Computer Systems and Networks, 2 (6), 2024 | |
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| dc.relation.references | 14. Wang F., Tang K., Li K., Liu Z., Zhu L. (2019). A Group-Based Signal Timing Optimization Model Considering Safety for Signalized Intersections with Mixed Traffic Flows, Journal of Advanced Transportation, vol.2019, DOI: https://doi.org/10.1155/2019/2747569 | |
| dc.relation.references | 15. Alkhatib A. A., Maria A. K., AlZu`bi S. (2022). Smart Traffic Scheduling for Crowded Cities Road Networks, Egyptian Informatics Journal, vol. 23(4), pp. 163–176. DOI: https://doi.org/10.1016/j.eij.2022.10.002 | |
| dc.relation.references | 16. Bo Liu, Zhentao Ding. (2022). A distributed deep reinforcement learning method for traffic light control.Neurocomputing. no.490, pp. 390–399 DOI: https://doi.org/10.1016/j.neucom.2021.11.106 | |
| dc.relation.references | 17. Hai D. T., Manh D. V., Nhat N. M. (2022). Genetic algorithm application for optimizing traffic signal timing reflecting vehicle emission intensity, Transport Problems, no.17(1), pp. 5–16 DOI:https://doi.org/10.20858/tp.2022.17.1.01 | |
| dc.relation.referencesen | 1. Wu J., Cheng L., Chu S., Song Y. (2024). An autonomous coverage path planning algorithm for maritime search and rescue of persons-in-water based on deep reinforcement learning. Ocean. Eng. 2024, 291, 116403. DOI: https://doi.org/10.1016/j.oceaneng.2023.116403 | |
| dc.relation.referencesen | 2. Ma Yue, Bo Li, Wentao Huang, and Qinqin Fan (2023.) An Improved NSGA-II Based on Multi-Task Optimization for Multi-UAV Maritime Search and Rescue under Severe Weather Journal of Marine Science and Engineering 11, no. 4: 781. DOI: https://doi.org/10.3390/jmse11040781 | |
| dc.relation.referencesen | 3. Cho S. W., Park H. J., Lee H., Shim D. H., Kim, S. Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations. Comput. Ind. Eng. 2021, 161 DOI: https://doi.org/10.1016/j.cie.2021.107612 | |
| dc.relation.referencesen | 4. Skinderowicz R. Improving Ant Colony Optimization efficiency for solving large TSP instances. Appl. Soft Comput. 2022, 120 DOI: https://doi.org/10.1016/j.asoc.2022.108653 | |
| dc.relation.referencesen | 5. Wang Y., Jiang Y., Wu Y., Yao Z. (2024). Mitigating traffic oscillation through control of connected automated vehicles: A cellular automata simulation, Expert Systems with Applications, no.235, DOI: https://doi.org/10.1016/j.eswa.2023.121275 | |
| dc.relation.referencesen | 6. Liu Yuxin, Zihang Qin, and Jin Liu. 2023. "An Improved Genetic Algorithm for the Granularity-Based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup" Mathematics 11, no. 15: 3328. https://doi.org/10.3390/math11153328 | |
| dc.relation.referencesen | 7. Sarbijan M. S.; Behnamian, J. A mathematical model and metaheuristic approach to solve the real-time feeder vehicle routing problem. Comput. Ind. Eng. 2023, DOI: https://doi.org/10.1016/j.cie.2023.109684 | |
| dc.relation.referencesen | 8. Wu, Y.; Cai, Y.; Fang, C. Evolutionary Multitasking for Bidirectional Adaptive Codec: A Case Study on Vehicle Routing Problem with Time Windows. Appl. Soft. Comput. 2023, 145, DOI: https://doi.org/10.1016/j.asoc.2023.110605 | |
| dc.relation.referencesen | 9. Sowmya, K.M., Rekha, B., Praveen, S.K. (2021). Real Time Moving Vehicle Congestion Detection and Tracking using OpenCV. Turkish Journal of Computer and Mathematics Education, 12(10), pp. 273–279. [Online]. –Available: https://www.turcomat.org/index.php/turkbilmat/article/view/4139 | |
| dc.relation.referencesen | 10. Abu-Alsaad, H.A. (2023) Cnn-Based Smart Parking System. International Journal of Interactive Mobile Technologies (iJIM), 17, 155-170. DOI: https://doi.org/10.3991/ijim.v17i11.37033 | |
| dc.relation.referencesen | 11. DSTU 4157: 2003 "Technical peripherals for automated traffic control systems" [Electronic resource]. –Available at: https://docs.dbn.co.ua/3641_1583178494026.html (Accessed: 3/02/2024) | |
| dc.relation.referencesen | 12. Yao Z., Li L., Liao W., Wang Y. (2024). Optimal lane management policy for connected automated vehicles in mixed traffic flow, Physica A: Statistical Mechanics and its Applications, no.637, DOI:https://doi.org/10.1016/j.physa.2024.129520 | |
| dc.relation.referencesen | 13. Liu K., Feng T. (2023). Heterogeneous traffic flow cellular automata model mixed with intelligent controlled vehicles, Physica A: Statistical Mechanics and its Applications, no.632, DOI:https://doi.org/10.1016/j.physa.2023.129316 | |
| dc.relation.referencesen | 14. Wang F., Tang K., Li K., Liu Z., Zhu L. (2019). A Group-Based Signal Timing Optimization Model Considering Safety for Signalized Intersections with Mixed Traffic Flows, Journal of Advanced Transportation, vol.2019, DOI: https://doi.org/10.1155/2019/2747569 | |
| dc.relation.referencesen | 15. Alkhatib A. A., Maria A. K., AlZu`bi S. (2022). Smart Traffic Scheduling for Crowded Cities Road Networks, Egyptian Informatics Journal, vol. 23(4), pp. 163–176. DOI: https://doi.org/10.1016/j.eij.2022.10.002 | |
| dc.relation.referencesen | 16. Bo Liu, Zhentao Ding. (2022). A distributed deep reinforcement learning method for traffic light control.Neurocomputing. no.490, pp. 390–399 DOI: https://doi.org/10.1016/j.neucom.2021.11.106 | |
| dc.relation.referencesen | 17. Hai D. T., Manh D. V., Nhat N. M. (2022). Genetic algorithm application for optimizing traffic signal timing reflecting vehicle emission intensity, Transport Problems, no.17(1), pp. 5–16 DOI:https://doi.org/10.20858/tp.2022.17.1.01 | |
| dc.relation.uri | https://doi.org/10.1016/j.oceaneng.2023.116403 | |
| dc.relation.uri | https://doi.org/10.3390/jmse11040781 | |
| dc.relation.uri | https://doi.org/10.1016/j.cie.2021.107612 | |
| dc.relation.uri | https://doi.org/10.1016/j.asoc.2022.108653 | |
| dc.relation.uri | https://doi.org/10.1016/j.eswa.2023.121275 | |
| dc.relation.uri | https://doi.org/10.3390/math11153328 | |
| dc.relation.uri | https://doi.org/10.1016/j.cie.2023.109684 | |
| dc.relation.uri | https://doi.org/10.1016/j.asoc.2023.110605 | |
| dc.relation.uri | https://www.turcomat.org/index.php/turkbilmat/article/view/4139 | |
| dc.relation.uri | https://doi.org/10.3991/ijim.v17i11.37033 | |
| dc.relation.uri | https://docs.dbn.co.ua/3641_1583178494026.html | |
| dc.relation.uri | https://doi.org/10.1016/j.physa.2024.129520 | |
| dc.relation.uri | https://doi.org/10.1016/j.physa.2023.129316 | |
| dc.relation.uri | https://doi.org/10.1155/2019/2747569 | |
| dc.relation.uri | https://doi.org/10.1016/j.eij.2022.10.002 | |
| dc.relation.uri | https://doi.org/10.1016/j.neucom.2021.11.106 | |
| dc.relation.uri | https://doi.org/10.20858/tp.2022.17.1.01 | |
| dc.rights.holder | © Національний університет „Львівська політехніка“, 2024 | |
| dc.rights.holder | © Данилюк А. Г., 2024 | |
| dc.subject | адаптивне управління трафіком | |
| dc.subject | затори | |
| dc.subject | кіберфізична система | |
| dc.subject | контролер світлофорів | |
| dc.subject | перехрестя | |
| dc.subject | трафік | |
| dc.subject | аdaptive traffic management | |
| dc.subject | cyber-physical system | |
| dc.subject | intersections | |
| dc.subject | jams | |
| dc.subject | traffic | |
| dc.subject | traffic light controller | |
| dc.subject.udc | 004.75 | |
| dc.title | Застосування алгоритму оптимізації мурашиної колонії в світлофорному керуванні | |
| dc.title.alternative | Application of ant colony optimization algorithm in road traffic control | |
| dc.type | Article |