Ant Colony Algorithm in Traffic Flow Control
| dc.citation.epage | 163 | |
| dc.citation.issue | 2 | |
| dc.citation.journalTitle | Досягнення у кіберфізичних системах | |
| dc.citation.spage | 158 | |
| dc.citation.volume | 9 | |
| dc.contributor.affiliation | Lviv Polytechnic National University | |
| dc.contributor.author | Danyliuk, Andrii | |
| dc.contributor.author | Muliarevych, Oleksandr | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2025-11-06T08:48:07Z | |
| dc.date.created | 2024-02-27 | |
| dc.date.issued | 2024-02-27 | |
| 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 | 158-163 | |
| dc.format.pages | 6 | |
| dc.identifier.citation | Danyliuk A. Ant Colony Algorithm in Traffic Flow Control / Andrii Danyliuk, Oleksandr Muliarevych // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 2. — P. 158–163. | |
| dc.identifier.citationen | Danyliuk A. Ant Colony Algorithm in Traffic Flow Control / Andrii Danyliuk, Oleksandr Muliarevych // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 2. — P. 158–163. | |
| dc.identifier.doi | doi.org/10.23939/acps2024.02.158 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/117375 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Досягнення у кіберфізичних системах, 2 (9), 2024 | |
| dc.relation.ispartof | Advances in Cyber-Physical Systems, 2 (9), 2024 | |
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| dc.relation.references | [11] 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.references | [12] 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 | |
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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] Nguyen, Tri-Hai & Jung, Jason. (2021). Ant colony optimization-based traffic routing with intersection negotiation for connected vehicles. Applied Soft Computing, 112. 107828. 10.1016/j.asoc.2021.107828. | |
| dc.relation.references | [16] Alkhatib A.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 | [17] 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 | [18] Hai D. T., Manh D. V., Nhat N. M. (2022). Genetic algo-rithm 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.references | [19] Abdou A. A., Farrag H. M., and A. S. Tolba (2022). A Fuzzy Logic-Based Smart Traffic Management Systems. Journal of Computer Science, no. 18(11), pp. 1085–1099 DOI: https://doi.org/10.3844/jcssp.2022.1085.1099 | |
| dc.relation.references | [20] Buzachis A., Celesti A., Galleta A., Fazio M., Fortino G., Villari M. (2020). A multi-agent autonomous intersection management (MA-AIM) system for smart cities leveraging edge-of-things and Blockchain. Information Sciences, no. 522, pp. 148–163. DOI: https://doi.org/10.1016/j.ins.2020.02.059 | |
| 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, 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. (2021) Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations. Comput. Ind. Eng., 161. DOI: https://doi.org/10.1016/j.cie.2021.107612 | |
| dc.relation.referencesen | [4] Skinderowicz, R. (2022). Improving Ant Colony Optimization efficiency for solving large TSP instances. Appl. Soft Comput., 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. (2023). A mathematical model and metaheuristic approach to solve the real-time feeder vehicle routing problem. Comput. Ind. Eng. 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] 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 | [10] P.-S. Shih, S. Liu and X.-H. Yu, "Ant Colony Optimization for Multi-phase Traffic Signal Control", 2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE). Beijing, China, 2022, pp. 517–521, DOI: 10.1109/ICITE56321.2022.10101431. | |
| dc.relation.referencesen | [11] 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 | [12] 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 | [13] Yulianto, B. (2023). Adaptive Traffic Signal Control Using Fuzzy Logic Under Mixed Traffic Conditions. In: Kristiawan, S. A., Gan, B. S., Shahin, M., Sharma, A. (eds). Proceedings of the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering. ICRMCE 2021. Lecture Notes in Civil Engineering, vol. 225. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-16-9348-9_59 | |
| 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] Nguyen, Tri-Hai & Jung, Jason. (2021). Ant colony optimization-based traffic routing with intersection negotiation for connected vehicles. Applied Soft Computing, 112. 107828. 10.1016/j.asoc.2021.107828. | |
| dc.relation.referencesen | [16] Alkhatib A.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 | [17] 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 | [18] Hai D. T., Manh D. V., Nhat N. M. (2022). Genetic algo-rithm 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 | [19] Abdou A. A., Farrag H. M., and A. S. Tolba (2022). A Fuzzy Logic-Based Smart Traffic Management Systems. Journal of Computer Science, no. 18(11), pp. 1085–1099 DOI: https://doi.org/10.3844/jcssp.2022.1085.1099 | |
| dc.relation.referencesen | [20] Buzachis A., Celesti A., Galleta A., Fazio M., Fortino G., Villari M. (2020). A multi-agent autonomous intersection management (MA-AIM) system for smart cities leveraging edge-of-things and Blockchain. Information Sciences, no. 522, pp. 148–163. DOI: https://doi.org/10.1016/j.ins.2020.02.059 | |
| 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://doi.org/10.3991/ijim.v17i11.37033 | |
| 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/10.1007/978-981-16-9348-9_59 | |
| 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.relation.uri | https://doi.org/10.3844/jcssp.2022.1085.1099 | |
| dc.relation.uri | https://doi.org/10.1016/j.ins.2020.02.059 | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2024 | |
| dc.rights.holder | © Danyliuk A., Muliarevych O., 2024 | |
| dc.subject | Traffic | |
| dc.subject | congestion | |
| dc.subject | intersection | |
| dc.subject | traffic light controller | |
| dc.subject | adaptive traffic control | |
| dc.subject | cyber-physical system | |
| dc.title | Ant Colony Algorithm in Traffic Flow Control | |
| dc.type | Article |
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