Abstract
Addressing traffic congestion holds paramount importance due to its severe economic and environmental repercussions. This study introduces an approach to address this pervasive issue by employing a wide-area control strategy for diverse road networks. The strategy leverages a dynamic offset control method and a multi-agent model to create a unique solution. In this framework, individual intersections function as distinct agents, engaging in negotiations, establishing connections, and forming a dynamic offset control zone resembling a tree structure. Within this structure, agents collaboratively manage green wave synchronization based on real-time traffic conditions at the network boundaries. To evaluate the effectiveness of this approach, comprehensive tests utilize both a simulated road network (Experiment 1) and an actual grid-like road network (Experiment 2). In Experiment 1, the proposed method consistently reduces lost time, resulting in an average reduction of 15% across all scenarios. Experiment 2 demonstrates a reduction in lost time across various intervals, with an impressive average reduction of 34% in lost time across all scenarios. These results demonstrate the strategy's ability to dynamically and adaptively establish green waves that significantly enhance traffic flow. In conclusion, this study demonstrates that the proposed method autonomously conducts offset control, effectively contributing to the smooth flow of vehicles.
Keywords
Traffic congestion Dynamic offset control Multi-agent model Green wave control Traffic flow enhancement
References
- Abdurakhmanov, R. (2022). Determination of Traffic Congestion and Delay of Traffic Flow At Controlled Intersections. The American Journal of Engineering and Technology, 4(10), 4-11.
- Alsaawy, Y., Alkhodre, A., Abi Sen, A., Alshanqiti, A., Bhat, W. A., & Bahbouh, N. M. (2022). A comprehensive and effective framework for traffic congestion problem based on the integration of IoT and data analytics. Applied Sciences, 12(4), 2043.
- Babaei, A., Khedmati, M., Jokar, M. R. A., & Tirkolaee, E. B. (2023). Sustainable transportation planning considering traffic congestion and uncertain conditions. Expert Systems with Applications, 227, 119792.
- Cao, M., Li, V. O., & Shuai, Q. (2022). Book Your Green Wave: Exploiting Navigation Information for Intelligent Traffic Signal Control. IEEE Transactions on Vehicular Technology, 71(8), 8225-8236.
- Chen, L. W., & Chang, C. C. (2016). Cooperative traffic control with green wave coordination for multiple intersections based on the internet of vehicles. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(7), 1321-1335.
- Ji, L., & Cheng, W. (2022). Method of Bidirectional Green Wave Coordinated Control for Arterials under Asymmetric Release Mode. Electronics, 11(18), 2846.
- Karimov, A. (2023). " Green Wave" Module for Creating An Artificial Intelligence-Based Adaptive Complex Of Road Network Permeability To Improve Road Traffic Safety. International Bulletin of Engineering and Technology, 3(3), 108-127.
- Khamis, M. A., & Gomaa, W. (2014). Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework. Engineering Applications of Artificial Intelligence, 29, 134-151.
- Lu, K., Jiang, S., Xin, W., Zhang, J., & He, K. (2022). Algebraic method of regional green wave coordinated control. Journal of Intelligent Transportation Systems, 1-19.
- Lu, K., Tian, X., Jiang, S., Lin, Y., & Zhang, W. (2023). Optimization Model of Regional Green Wave Coordination Control for the Coordinated Path Set. IEEE Transactions on Intelligent Transportation Systems.
- Ma, C., & He, R. (2019). Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm. Neural Computing and Applications, 31, 2073-2083.
- Soon, K. L., Lim, J. M. Y., & Parthiban, R. (2019). Coordinated traffic light control in cooperative green vehicle routing for pheromone-based multi-agent systems. Applied Soft Computing, 81, 105486.
- Tobita, K., & Nagatani, T. (2013). Green-wave control of an unbalanced two-route traffic system with signals. Physica A: Statistical Mechanics and its Applications, 392(21), 5422-5430.
- Wang, T., Cao, J., & Hussain, A. (2021). Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning. Transportation research part C: emerging technologies, 125, 103046.
- Wu, X., Deng, S., Du, X., & Ma, J. (2014). Green-wave traffic theory optimization and analysis. World Journal of Engineering and Technology, 2(3), 14-19.
- Yang, S. (2023). Hierarchical graph multi-agent reinforcement learning for traffic signal control. Information Sciences, 634, 55-72.
- Yang, S., & Yang, B. (2022). An inductive heterogeneous graph attention-based multi-agent deep graph infomax algorithm for adaptive traffic signal control. Information Fusion, 88, 249-262.
- Yuan, S., Xu, S., & Zheng, S. (2022, January). Deep reinforcement learning based green wave speed guidance for human-driven connected vehicles at signalized intersections. In 2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) (pp. 331-339). IEEE.
- Zhu, L., Wang, J. X., Dai, S., & Wu, J. Y. (2023). A phase sequence optimization method oriented by ideal bidirectional green wave. In Advances in Urban Construction and Management Engineering (pp. 563-570). CRC Press.
Abstract
Trafik sıkışıklığının giderilmesi, ciddi ekonomik ve çevresel yansımaları nedeniyle büyük önem taşıyor. Bu çalışma, çeşitli yol ağları için geniş alanlı bir kontrol stratejisi kullanarak bu yaygın sorunu çözmeye yönelik bir yaklaşım sunmaktadır. Strateji, benzersiz bir çözüm oluşturmak için dinamik bir dengeleme kontrol yönteminden ve çok etmenli bir modelden yararlanır. Bu çerçevede bireysel kesişimler, müzakerelere katılan, bağlantılar kuran ve bir ağaç yapısına benzeyen dinamik bir dengeleme kontrol bölgesi oluşturan ayrı aktörler olarak işlev görür. Bu yapı içerisinde aracılar, ağ sınırlarındaki gerçek zamanlı trafik koşullarına dayalı olarak yeşil dalga senkronizasyonunu işbirliği içinde yönetir. Bu yaklaşımın etkinliğini değerlendirmek için, kapsamlı testler hem simüle edilmiş bir yol ağını (Deney 1) hem de gerçek ızgara benzeri bir yol ağını (Deney 2) kullanır. Deney 1'de önerilen yöntem, kayıp zamanı sürekli olarak azaltarak tüm senaryolarda ortalama %15'lik bir azalmaya yol açtı. Deney 2, tüm senaryolarda kayıp sürede ortalama %34'lük etkileyici bir azalmayla, çeşitli aralıklarla kayıp sürede bir azalma olduğunu göstermektedir. Bu sonuçlar, stratejinin trafik akışını önemli ölçüde artıran yeşil dalgaları dinamik ve uyarlanabilir bir şekilde oluşturma yeteneğini göstermektedir. Sonuç olarak bu çalışma, önerilen yöntemin otonom olarak ofset kontrolü gerçekleştirdiğini ve araçların düzgün akışına etkili bir şekilde katkıda bulunduğunu göstermektedir.
Keywords
Trafik sıkışıklığı Dinamik ofset kontrolü Çok etmenli model Yeşil dalga kontrolü Trafik akışı geliştirme
References
- Abdurakhmanov, R. (2022). Determination of Traffic Congestion and Delay of Traffic Flow At Controlled Intersections. The American Journal of Engineering and Technology, 4(10), 4-11.
- Alsaawy, Y., Alkhodre, A., Abi Sen, A., Alshanqiti, A., Bhat, W. A., & Bahbouh, N. M. (2022). A comprehensive and effective framework for traffic congestion problem based on the integration of IoT and data analytics. Applied Sciences, 12(4), 2043.
- Babaei, A., Khedmati, M., Jokar, M. R. A., & Tirkolaee, E. B. (2023). Sustainable transportation planning considering traffic congestion and uncertain conditions. Expert Systems with Applications, 227, 119792.
- Cao, M., Li, V. O., & Shuai, Q. (2022). Book Your Green Wave: Exploiting Navigation Information for Intelligent Traffic Signal Control. IEEE Transactions on Vehicular Technology, 71(8), 8225-8236.
- Chen, L. W., & Chang, C. C. (2016). Cooperative traffic control with green wave coordination for multiple intersections based on the internet of vehicles. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(7), 1321-1335.
- Ji, L., & Cheng, W. (2022). Method of Bidirectional Green Wave Coordinated Control for Arterials under Asymmetric Release Mode. Electronics, 11(18), 2846.
- Karimov, A. (2023). " Green Wave" Module for Creating An Artificial Intelligence-Based Adaptive Complex Of Road Network Permeability To Improve Road Traffic Safety. International Bulletin of Engineering and Technology, 3(3), 108-127.
- Khamis, M. A., & Gomaa, W. (2014). Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework. Engineering Applications of Artificial Intelligence, 29, 134-151.
- Lu, K., Jiang, S., Xin, W., Zhang, J., & He, K. (2022). Algebraic method of regional green wave coordinated control. Journal of Intelligent Transportation Systems, 1-19.
- Lu, K., Tian, X., Jiang, S., Lin, Y., & Zhang, W. (2023). Optimization Model of Regional Green Wave Coordination Control for the Coordinated Path Set. IEEE Transactions on Intelligent Transportation Systems.
- Ma, C., & He, R. (2019). Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm. Neural Computing and Applications, 31, 2073-2083.
- Soon, K. L., Lim, J. M. Y., & Parthiban, R. (2019). Coordinated traffic light control in cooperative green vehicle routing for pheromone-based multi-agent systems. Applied Soft Computing, 81, 105486.
- Tobita, K., & Nagatani, T. (2013). Green-wave control of an unbalanced two-route traffic system with signals. Physica A: Statistical Mechanics and its Applications, 392(21), 5422-5430.
- Wang, T., Cao, J., & Hussain, A. (2021). Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning. Transportation research part C: emerging technologies, 125, 103046.
- Wu, X., Deng, S., Du, X., & Ma, J. (2014). Green-wave traffic theory optimization and analysis. World Journal of Engineering and Technology, 2(3), 14-19.
- Yang, S. (2023). Hierarchical graph multi-agent reinforcement learning for traffic signal control. Information Sciences, 634, 55-72.
- Yang, S., & Yang, B. (2022). An inductive heterogeneous graph attention-based multi-agent deep graph infomax algorithm for adaptive traffic signal control. Information Fusion, 88, 249-262.
- Yuan, S., Xu, S., & Zheng, S. (2022, January). Deep reinforcement learning based green wave speed guidance for human-driven connected vehicles at signalized intersections. In 2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) (pp. 331-339). IEEE.
- Zhu, L., Wang, J. X., Dai, S., & Wu, J. Y. (2023). A phase sequence optimization method oriented by ideal bidirectional green wave. In Advances in Urban Construction and Management Engineering (pp. 563-570). CRC Press.
Details
| Primary Language | English |
|---|---|
| Subjects | Autonomous Agents and Multiagent Systems |
| Journal Section | Computer Engineering |
| Authors | |
| Publication Date | December 12, 2023 |
| Submission Date | August 1, 2023 |
| Published in Issue | Year 2023Volume: 26 Issue: Özel Sayı - 9th Uluslararası IFS Çağdaş Matematik ve Mühendislik Konferansı (IFSCOM-E) Özel Sayısı |
