Multi-agent modeling of traffic organization in urban agglomerations
dc.citation.epage | 22 | |
dc.citation.issue | 1 | |
dc.citation.spage | 10 | |
dc.contributor.affiliation | National University of Life and Environmental Sciences of Ukraine | |
dc.contributor.author | Weigang, Ganna | |
dc.contributor.author | Komar, Kateryna | |
dc.coverage.placename | Львів | |
dc.coverage.placename | Lviv | |
dc.date.accessioned | 2024-06-27T09:07:51Z | |
dc.date.available | 2024-06-27T09:07:51Z | |
dc.date.created | 2024-02-28 | |
dc.date.issued | 2024-02-28 | |
dc.description.abstract | Розглянуто особливості мультиагентного моделювання для оптимізації дорожнього руху в центральних районах міст. Оцінюючи унікальні виклики, пов’язані з високою концентрацією транспорту, пішоходів та історичної забудови, досліджено потенціал мультиагентних систем для ефективного вирішення проблеми заторів, безпеки та якості життя у міських умовах. Потенціал мультиагентного моделювання в контексті управління дорожнім рухом у центральних районах міста дає змогу визначити ключові виклики та можливості. Багато науковців звертаються до основних аспектів такого моделювання і використовують їх у транспортно-дорожній галузі. Огляд сучасних досліджень та розробок показав, що мультиагентні моделі мають на меті імітувати та оптимізувати нагляд і контроль перевезень у різних сценаріях руху. Моделювання організації дорожнього руху в центральних районах міст є одним із ключових елементів планування міського розвитку та управління. Унаслідок зростання населення міст та збільшення кількості транспортних засобів проблеми заторів, забруднення повітря та неефективного використання інфраструктури стають дедалі актуальнішими. Тому можна зазначити, що мультиагентне моделювання організації дорожнього руху відкриває нові перспективи для розроблення ефективних стратегій управління транспортними потоками, забезпечуючи гнучке та адаптивне вирішення цих проблем. Проаналізовано використовувані підходи, визначено ключові компоненти системи і на основі математичного опису розроблено модель, яка демонструє взаємодію між агентами і середовищем. Практична симуляція моделі, виконана із використанням програмного забезпечення AnyLogic на прикладі бульвару Лесі Українки в м. Києві, підтверджує ефективність мультиагентного підходу. Результати дослідження вказують на можливість застосування розробленої моделі для вдосконалення інтелектуальних інформаційних систем управління транспортним потоком, що відкриває нові перспективи для покращення дорожнього руху в центральних районах міст. | |
dc.description.abstract | The authors consider the features of multi-agent modeling for traffic optimization in the central areas of cities. While evaluating the unique challenges associated with the high concentration of vehicles, pedestrians and historical buildings, the potential of multi-agent systems to effectively solve the problem of congestion, safety and quality of life in urban areas is investigated. The potential of multi-agent modeling in the context of traffic management in the central areas of the city allows us to identify the key challenges and opportunities. Many scientists address the main aspects of such modeling and use them in the transport and road sectors. A review of current research and development has shown that multi-agent models aim to simulate and optimize the supervision and control of transportation in various traffic scenarios. Modeling traffic organization in the central areas of cities is one of the main elements of urban development planning and management. Due to the growing population of cities and the increasing number of vehicles, the problems of congestion, air pollution, and inefficient use of infrastructure are becoming increasingly relevant. Therefore, it can be noted that multi-agent traffic modeling opens up new prospects for developing effective traffic management strategies, providing a flexible and adaptive solution to these problems. The research analyzes the existing approaches, identifies the system`s key components, and develops a model that demonstrates the interaction between agents and the environment based on a mathematical description. A practical simulation of the model, carried out using the AnyLogic software on the example of Lesia Ukrainka Boulevard in Kyiv, confirms the effectiveness of the multi-agent approach. The results of the study indicate the possibility of applying the developed model to improve intelligent information systems for traffic flow management, which opens up new prospects for improving traffic in the central areas of cities. | |
dc.format.extent | 10-22 | |
dc.format.pages | 13 | |
dc.identifier.citation | Weigang G. Multi-agent modeling of traffic organization in urban agglomerations / Ganna Weigang, Kateryna Komar // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 5. — No 1. — P. 10–22. | |
dc.identifier.citationen | Weigang G. Multi-agent modeling of traffic organization in urban agglomerations / Ganna Weigang, Kateryna Komar // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 5. — No 1. — P. 10–22. | |
dc.identifier.doi | doi.org/10.23939/tt2024.01.010 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/62290 | |
dc.language.iso | en | |
dc.publisher | Видавництво Львівської політехніки | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Transport Technologies, 1 (5), 2024 | |
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dc.relation.references | 3. Wang, J., Lv, W., Jiang, Y., Qin, S., & Li, J. (2021). A multi-agent based cellular automata model for intersection traffic control simulation. Physica A: Statistical Mechanics and its Applications, 584, 126356. doi: 10.1016/J.PHYSA.2021.126356 (in English). https://doi.org/10.1016/j.physa.2021.126356 | |
dc.relation.references | 4. Wang, S., & Wang, S. (2023). A Novel Multi-Agent Deep RL Approach for Traffic Signal Control. Computer Science. Retrieved from: https://arxiv.org/abs/2306.02684. (in English). | |
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dc.relation.references | 7. Zhuang, H., Lei, C., Chen, Y., & Tan, X. (2023). Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning. Applied Sciences, 13(8), 5018. doi: 10.3390/app13085018 (in English). https://doi.org/10.3390/app13085018 | |
dc.relation.references | 8. Mushtaq, A., Haq, I. U., Sarwar, M. A., Khan, A., Khalil, W., & Mughal, M. A. (2023). Multi-agent reinforcement learning for traffic flow management of autonomous vehicles. Sensors, 23(5), 2373. doi: 10.3390/s23052373 (in English). https://doi.org/10.3390/s23052373 | |
dc.relation.references | 9. Le, N. T. T. (2023). Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways. Journal of Information and Telecommunication, 7(3), 255-269. doi: 10.1080/24751839.2023.2182174 (in English). https://doi.org/10.1080/24751839.2023.2182174 | |
dc.relation.references | 10. Liu, Q., Li, Z., Li, X., Wu, J., & Yuan, S. (2022, October). Graph convolution-based deep reinforcement learning for multi-agent decision-making in interactive traffic scenarios. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 4074-4081). IEEE. doi: 10.1109/ITSC55140.2022.9922001 (in English). https://doi.org/10.1109/ITSC55140.2022.9922001 | |
dc.relation.references | 11. Zouari, M., Baklouti, N., Kammoun, M. H., Ayed, M. B., Alimi, A. M., & Sanchez-Medina, J. (2021, July). A multi-agent system for road traffic decision making based on hierarchical interval type-2 fuzzy knowledge representation system. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-6). IEEE. doi: 10.1109/FUZZ45933.2021.9494502 (in English). https://doi.org/10.1109/FUZZ45933.2021.9494502 | |
dc.relation.references | 12. Bastarianto, F. F., Hancock, T. O., Choudhury, C. F., & Manley, E. (2023). Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review, 15(1), 19. doi: 10.1186/s12544-023-00590-5 (in English). https://doi.org/10.1186/s12544-023-00590-5 | |
dc.relation.references | 13. Hamza, A., Rizvi, S. T. H., Safder, M. U., & Asif, H. (2022). A Novel Mathematical Approach to Model Multi-Agent-Based Main Grid and Microgrid Networks for Complete System Analysis. Machines, 10(2), 110. doi: 10.3390/machines10020110 (in English). https://doi.org/10.3390/machines10020110 | |
dc.relation.references | 14. Mathematical modeling of multi-agent search & task allocation. Retrieved from: https://www.duo.uio.no/bitstream/handle/10852/105314/Mathematical-modeli... (in English). | |
dc.relation.referencesen | 1. Titarmare, A. S., Khanapurkar, M. M., & Chandankhede, P. H. (2020). Analysis of traffic flow at intersection to avoid accidents using Nagel-Schreckenlerg model. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (pp. 478-484). IEEE. doi: 10.1109/I-SMAC49090.2020.9243306 (in English). https://doi.org/10.1109/I-SMAC49090.2020.9243306 | |
dc.relation.referencesen | 2. Dukić, A., Bjelošević, R., Stojčić, M., & Banjanin, M. K. (2023). Network Model of Multiagent Communication of Traffic Inspection for Supervision and Control of Passenger Transportation in Road and City Traffic. In 2023 46th MIPRO ICT and Electronics Convention (MIPRO) (pp. 1167-1172). IEEE. doi: 10.23919/MIPRO57284.2023.10159771 (in English). https://doi.org/10.23919/MIPRO57284.2023.10159771 | |
dc.relation.referencesen | 3. Wang, J., Lv, W., Jiang, Y., Qin, S., & Li, J. (2021). A multi-agent based cellular automata model for intersection traffic control simulation. Physica A: Statistical Mechanics and its Applications, 584, 126356. doi: 10.1016/J.PHYSA.2021.126356 (in English). https://doi.org/10.1016/j.physa.2021.126356 | |
dc.relation.referencesen | 4. Wang, S., & Wang, S. (2023). A Novel Multi-Agent Deep RL Approach for Traffic Signal Control. Computer Science. Retrieved from: https://arxiv.org/abs/2306.02684. (in English). | |
dc.relation.referencesen | 5. Liu, D., & Li, L. (2023). A traffic light control method based on multi-agent deep reinforcement learning algorithm. Scientific Reports, 13(1), 9396. doi: 10.1038/s41598-023-36606-2 (in English). https://doi.org/10.1038/s41598-023-36606-2 | |
dc.relation.referencesen | 6. Learning Multi-intersection Traffic Signal Control via Coevolutionary Multi-Agent Reinforcement Learning. Retrieved from: https://www.techrxiv.org/doi/full/10.36227/techrxiv.23254547.v1 (in English). | |
dc.relation.referencesen | 7. Zhuang, H., Lei, C., Chen, Y., & Tan, X. (2023). Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning. Applied Sciences, 13(8), 5018. doi: 10.3390/app13085018 (in English). https://doi.org/10.3390/app13085018 | |
dc.relation.referencesen | 8. Mushtaq, A., Haq, I. U., Sarwar, M. A., Khan, A., Khalil, W., & Mughal, M. A. (2023). Multi-agent reinforcement learning for traffic flow management of autonomous vehicles. Sensors, 23(5), 2373. doi: 10.3390/s23052373 (in English). https://doi.org/10.3390/s23052373 | |
dc.relation.referencesen | 9. Le, N. T. T. (2023). Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways. Journal of Information and Telecommunication, 7(3), 255-269. doi: 10.1080/24751839.2023.2182174 (in English). https://doi.org/10.1080/24751839.2023.2182174 | |
dc.relation.referencesen | 10. Liu, Q., Li, Z., Li, X., Wu, J., & Yuan, S. (2022, October). Graph convolution-based deep reinforcement learning for multi-agent decision-making in interactive traffic scenarios. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 4074-4081). IEEE. doi: 10.1109/ITSC55140.2022.9922001 (in English). https://doi.org/10.1109/ITSC55140.2022.9922001 | |
dc.relation.referencesen | 11. Zouari, M., Baklouti, N., Kammoun, M. H., Ayed, M. B., Alimi, A. M., & Sanchez-Medina, J. (2021, July). A multi-agent system for road traffic decision making based on hierarchical interval type-2 fuzzy knowledge representation system. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-6). IEEE. doi: 10.1109/FUZZ45933.2021.9494502 (in English). https://doi.org/10.1109/FUZZ45933.2021.9494502 | |
dc.relation.referencesen | 12. Bastarianto, F. F., Hancock, T. O., Choudhury, C. F., & Manley, E. (2023). Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review, 15(1), 19. doi: 10.1186/s12544-023-00590-5 (in English). https://doi.org/10.1186/s12544-023-00590-5 | |
dc.relation.referencesen | 13. Hamza, A., Rizvi, S. T. H., Safder, M. U., & Asif, H. (2022). A Novel Mathematical Approach to Model Multi-Agent-Based Main Grid and Microgrid Networks for Complete System Analysis. Machines, 10(2), 110. doi: 10.3390/machines10020110 (in English). https://doi.org/10.3390/machines10020110 | |
dc.relation.referencesen | 14. Mathematical modeling of multi-agent search & task allocation. Retrieved from: https://www.duo.uio.no/bitstream/handle/10852/105314/Mathematical-modeli... (in English). | |
dc.relation.uri | https://doi.org/10.1109/I-SMAC49090.2020.9243306 | |
dc.relation.uri | https://doi.org/10.23919/MIPRO57284.2023.10159771 | |
dc.relation.uri | https://doi.org/10.1016/j.physa.2021.126356 | |
dc.relation.uri | https://arxiv.org/abs/2306.02684 | |
dc.relation.uri | https://doi.org/10.1038/s41598-023-36606-2 | |
dc.relation.uri | https://www.techrxiv.org/doi/full/10.36227/techrxiv.23254547.v1 | |
dc.relation.uri | https://doi.org/10.3390/app13085018 | |
dc.relation.uri | https://doi.org/10.3390/s23052373 | |
dc.relation.uri | https://doi.org/10.1080/24751839.2023.2182174 | |
dc.relation.uri | https://doi.org/10.1109/ITSC55140.2022.9922001 | |
dc.relation.uri | https://doi.org/10.1109/FUZZ45933.2021.9494502 | |
dc.relation.uri | https://doi.org/10.1186/s12544-023-00590-5 | |
dc.relation.uri | https://doi.org/10.3390/machines10020110 | |
dc.relation.uri | https://www.duo.uio.no/bitstream/handle/10852/105314/Mathematical-modeli.. | |
dc.rights.holder | © Національний університет “Львівська політехніка”, 2024 | |
dc.rights.holder | © G. Weigang, K. Komar, 2024 | |
dc.subject | мультиагентне моделювання | |
dc.subject | дорожній рух | |
dc.subject | інформаційні системи | |
dc.subject | оптимізація | |
dc.subject | транспортний потік | |
dc.subject | AnyLogic | |
dc.subject | агент | |
dc.subject | математична модель | |
dc.subject | інтелектуальні системи | |
dc.subject | multi-agent modeling | |
dc.subject | traffic | |
dc.subject | information systems | |
dc.subject | optimization | |
dc.subject | traffic flow | |
dc.subject | AnyLogic | |
dc.subject | agent | |
dc.subject | mathematical model | |
dc.subject | intelligent systems | |
dc.title | Multi-agent modeling of traffic organization in urban agglomerations | |
dc.title.alternative | Мультиагентне моделювання організації дорожнього руху в міських агломераціях | |
dc.type | Article |
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