Features of the application of traffic flow management methods and tools
| dc.citation.epage | 74 | |
| dc.citation.issue | 1 | |
| dc.citation.journalTitle | Досягнення у кібер-фізичних системах | |
| dc.citation.spage | 68 | |
| 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 | 2025-03-17T10:07:59Z | |
| dc.date.created | 2024-02-27 | |
| dc.date.issued | 2024-02-27 | |
| dc.description.abstract | This article examines the causes and consequences of traffic jams, describes typical traffic flow behavior and analyzes traffic control methods and means. The paper demonstrates the proposed classification of traffic lights by type of regulation. In summary, the article represents a detailed overview of existing cyber-physical traffic control systems, such as SEA TCS, InSync and MASSTR. The article analyzes the existing methods of traffic regulation, examines the causes and consequences of congestion, the division of intersections into regulated and unregulated, and the classification of traffic lights by type of traffic control. Among the main parameters of traffic flow used by cyberphysical traffic control systems, the primary and most used are speed, density, and volume of vehicles. The article also reviews the existing cyber-physical traffic control systems and the primary technologies. | |
| dc.format.extent | 68-74 | |
| dc.format.pages | 7 | |
| dc.identifier.citation | Danyliuk A. Features of the application of traffic flow management methods and tools / Danyliuk Andrii, Muliarevych Oleksandr // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 1. — P. 68–74. | |
| dc.identifier.citationen | Danyliuk A. Features of the application of traffic flow management methods and tools / Danyliuk Andrii, Muliarevych Oleksandr // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 9. — No 1. — P. 68–74. | |
| dc.identifier.doi | doi.org/10.23939/acps2024.01.068 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/64182 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Досягнення у кібер-фізичних системах, 1 (9), 2024 | |
| dc.relation.ispartof | Advances in Cyber-Physical Systems, 1 (9), 2024 | |
| dc.relation.references | [1] Atta A., Abbas S., Khan M. A., Ahmed G., Farooq U. (2020). An adaptive approach: smart traffic congestion control system. Journal of King Saud University – Computer and Information Sciences, No. 32 (9), pp. 1012–1019 DOI: https://doi.org/10.1016/j. jksuci.2018.10.011 | |
| dc.relation.references | [2] Noaeen M. M., Naik A., Goodman L., Crebo J. (2022). Reinforcement learning in urban network traffic signal control: A systematic literature review. Expert Systems with Applications, No. 1 (8), pp. 16830. DOI: https://doi.org/10.1016/j.eswa.2022.116830 | |
| dc.relation.references | [3] Fastiuk Y., Bachynskyy R., Huzynets N. (2021). Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework. Advances in Cyber-Physical Systems, No. 4(2), pp. 105–111. DOI: https://doi.org/10.23939/acps2021.02.105 | |
| dc.relation.references | [4] Michelucci, U. (2019). Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection, 1st edition, TOELT LLC, Dübendorf, Switzerland, September 29 2019, 303 p. DOI: https://doi.org/10.1007/978-1-4842-4976-5 | |
| dc.relation.references | [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.references | [6] 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. Available: https://www.turcomat.org/index.php/turkbilmat/article/view/4139 (Accessed: 3/02/2024) | |
| dc.relation.references | [7] DSTU 4157: 2003 “Technical peripherals for automated traffic control systems”. Available at: https://docs.dbn.co.ua/3641_1583178494026.html (Accessed: 3/02/2024) | |
| dc.relation.references | [8] 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 | [9] 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.references | [10] 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 | [11] 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 | [12] 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 | [13] 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.references | [14] 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 | [15] 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.references | [16] High Accuracy Traffic Light Controller for Increasing the Given Green Time Utilization – Scientific Figure on ResearchGate. [Online]. Available: https://www.researchgate.net/figure/Standard-four-legsintersection_fig3_270393077 [accessed 26 Apr, 2024] | |
| dc.relation.references | [17] Emergence Of Traffic Lights Synchronization – ScientificFigure on ResearchGate. [Online]. Available: https://www.researchgate.net/figure/Example-of-a-greenwave-causing-a-traffic-jam-in-secondarystreets_fig1_228566419 [accessed 26 Apr, 2024] | |
| dc.relation.references | [18] High Density Traffic Management using Image background subtraction Algorithm – Scientific Figure on ResearchGate. [Online]. Available: https://www.researchgate.net/figure/Inductive-loopdetectors-based-traffic-management_fig1_274270897 [accessed 26 Apr, 2024] | |
| dc.relation.references | [19] New Directions in Traffic Control Analysis through Video Surveillance - Scientific Figure on ResearchGate. [Online]. Available: https://www.researchgate.net/figure/VehicleDetection-in-video-Stream-b-Vehicleclassification_fig3_355121055 [accessed 26 Apr, 2024 | |
| dc.relation.referencesen | [1] Atta A., Abbas S., Khan M. A., Ahmed G., Farooq U. (2020). An adaptive approach: smart traffic congestion control system. Journal of King Saud University – Computer and Information Sciences, No. 32 (9), pp. 1012–1019 DOI: https://doi.org/10.1016/j. jksuci.2018.10.011 | |
| dc.relation.referencesen | [2] Noaeen M. M., Naik A., Goodman L., Crebo J. (2022). Reinforcement learning in urban network traffic signal control: A systematic literature review. Expert Systems with Applications, No. 1 (8), pp. 16830. DOI: https://doi.org/10.1016/j.eswa.2022.116830 | |
| dc.relation.referencesen | [3] Fastiuk Y., Bachynskyy R., Huzynets N. (2021). Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework. Advances in Cyber-Physical Systems, No. 4(2), pp. 105–111. DOI: https://doi.org/10.23939/acps2021.02.105 | |
| dc.relation.referencesen | [4] Michelucci, U. (2019). Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection, 1st edition, TOELT LLC, Dübendorf, Switzerland, September 29 2019, 303 p. DOI: https://doi.org/10.1007/978-1-4842-4976-5 | |
| 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] 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. Available: https://www.turcomat.org/index.php/turkbilmat/article/view/4139 (Accessed: 3/02/2024) | |
| dc.relation.referencesen | [7] DSTU 4157: 2003 "Technical peripherals for automated traffic control systems". Available at: https://docs.dbn.co.ua/3641_1583178494026.html (Accessed: 3/02/2024) | |
| dc.relation.referencesen | [8] 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 | [9] 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 | [10] 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 | [11] 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 | [12] 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 | [13] 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 | [14] 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 | [15] 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 | [16] High Accuracy Traffic Light Controller for Increasing the Given Green Time Utilization – Scientific Figure on ResearchGate. [Online]. Available: https://www.researchgate.net/figure/Standard-four-legsintersection_fig3_270393077 [accessed 26 Apr, 2024] | |
| dc.relation.referencesen | [17] Emergence Of Traffic Lights Synchronization – ScientificFigure on ResearchGate. [Online]. Available: https://www.researchgate.net/figure/Example-of-a-greenwave-causing-a-traffic-jam-in-secondarystreets_fig1_228566419 [accessed 26 Apr, 2024] | |
| dc.relation.referencesen | [18] High Density Traffic Management using Image background subtraction Algorithm – Scientific Figure on ResearchGate. [Online]. Available: https://www.researchgate.net/figure/Inductive-loopdetectors-based-traffic-management_fig1_274270897 [accessed 26 Apr, 2024] | |
| dc.relation.referencesen | [19] New Directions in Traffic Control Analysis through Video Surveillance - Scientific Figure on ResearchGate. [Online]. Available: https://www.researchgate.net/figure/VehicleDetection-in-video-Stream-b-Vehicleclassification_fig3_355121055 [accessed 26 Apr, 2024 | |
| dc.relation.uri | https://doi.org/10.1016/j | |
| dc.relation.uri | https://doi.org/10.1016/j.eswa.2022.116830 | |
| dc.relation.uri | https://doi.org/10.23939/acps2021.02.105 | |
| dc.relation.uri | https://doi.org/10.1007/978-1-4842-4976-5 | |
| dc.relation.uri | https://doi.org/10.1016/j.eswa.2023.121275 | |
| dc.relation.uri | https://www.turcomat.org/index.php/turkbilmat/article/view/4139 | |
| 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.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.relation.uri | https://www.researchgate.net/figure/Standard-four-legsintersection_fig3_270393077 | |
| dc.relation.uri | https://www.researchgate.net/figure/Example-of-a-greenwave-causing-a-traffic-jam-in-secondarystreets_fig1_228566419 | |
| dc.relation.uri | https://www.researchgate.net/figure/Inductive-loopdetectors-based-traffic-management_fig1_274270897 | |
| dc.relation.uri | https://www.researchgate.net/figure/VehicleDetection-in-video-Stream-b-Vehicleclassification_fig3_355121055 | |
| 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 | Features of the application of traffic flow management methods and tools | |
| dc.type | Article |
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