Comparison of Optimization Techniques for Delay Minimization in Signalized Intersections: PSO vs GA

Yıl 2023, , 162 - 172, 31.08.2023

Abdullah Karadağ , Murat Ergün

https://doi.org/10.31590/ejosat.1270905

Öz

Minimizing intersection delays is an important challenge in today’s smart cities. Even there are different approaches for delay minimization most of them uses the same nonlinear delay formula defined by Highway Capacity Manual (US). As a result choosing a fast and precise algorithm for finding the optimum inputs minimizing the delay output is a critical decision. In this paper we share our experience in selection of best optimization algorithm as a part of our work of developing an innovative system to minimize person delays in intersections. We compared two best known algorithms: Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). It is shown that using the same population size and number of iterations PSO is 7x faster and 17x more precise than GA.

Anahtar Kelimeler

Adaptive traffic signal control, Genetic algorithms, Particle swarm optimization, Intersection delay minimization, Real-time optimization.

Destekleyen Kurum

Yok

Proje Numarası

Yok

Teşekkür

First of all, all praise and thanks be to Allah. Next, thanks to Istanbul Metropolitan Municipality for their material and data support. Finally special thanks to Assoc. Prof. Sirma Yavuz, Assoc. Prof. H.Onur Tezcan for their valuable guidance.

Sinyalize Kavşaklarda Gecikmeyi Minimize Etmekte Kullanılan Optimizasyon Tekniklerinin Karşılaştırılması: PSO ve GA

Öz

Kavşak gecikmelerini en aza indirmek günümüzün akıllı şehirlerinde önemli bir zorluktur. Gecikme minimizasyonu için farklı yaklaşımlar olsa da bunların çoğu Karayolu Kapasite El Kitabı (ABD) tarafından tanımlanan aynı doğrusal olmayan gecikme formülünü kullanır. Sonuç olarak, gecikme çıktısını en aza indiren optimum girdileri bulmak için hızlı ve hassas bir algoritma seçmek kritik bir karardır. Bu makalede, kavşaklardaki kişi gecikmelerini en aza indirmek için yenilikçi bir sistem geliştirme çalışmamızın bir parçası olarak en iyi optimizasyon algoritmasının seçimindeki deneyimimizi paylaşıyoruz. En iyi bilinen iki algoritmayı karşılaştırdık: Genetik Algoritmalar (GA) ve Parçacık Sürü Optimizasyonu (PSO). Aynı popülasyon boyutu ve iterasyon sayısı kullanıldığında PSO'nun GA'dan 7 kat daha hızlı ve 17 kat daha hassas olduğu gösterilmiştir.

Anahtar Kelimeler

Adaptif trafik sinyal kontrolü, Genetik algoritmalar, Parçacık sürü optimizasyonu, Kavşak gecikme minimizasyonu, Gerçek zamanlı eniyileme

Proje Numarası

Yok

Kaynakça

• Akgüngör, A., Yılmaz, Ö., Korkmaz, E., & Doğan, E. (2019). Meta-Sezgisel Yöntemlerle Sabit Zamanlı Sinyalize Kavşaklar için Optimum Devre Süresi Modeli. El-Cezeri, 6(2), 259–269. https://doi.org/10.31202/ECJSE.496257
• Cakici, Z., & Murat, Y. S. (2019). A Differential Evolution Algorithm-Based Traffic Control Model for Signalized Intersections. Advances in Civil Engineering, 2019, 7360939. https://doi.org/10.1155/2019/7360939
• Çakici, Z., & Murat, Y. Ş. (2021). Sinyalize Dönel Kavşaklarda Diferansiyel Gelişim Algoritması ile Sinyal Süre Optimizasyonu. El-Cezeri, 8(2), 635–651. https://doi.org/10.31202/ECJSE.861429
• Clerc, M. (1999). The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, 3, 1951–1957. https://doi.org/10.1109/CEC.1999.785513
• Dong, C., Huang, S., & Liu, X. (2010). Urban Area Traffic Signal Timing Optimization Based on Sa-PSO. 2010 International Conference on Artificial Intelligence and Computational Intelligence, 3, 80–84. https://doi.org/10.1109/AICI.2010.257
• Eberhart, R. C., & Shi, Y. (1998). Comparison between genetic algorithms and particle swarm optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1447, 611–616. https://doi.org/10.1007/BFB0040812/COVER
• Erdoğmuş, P. (2010). (1) (PDF) Particle swarm optimization performance on special linear programming problems. Scientific Research and Essays, 5(12), 1506–1518. https://www.researchgate.net/publication/229041601_Particle_swarm_optimization_performance_on_speci al_linear_programming_problems
• Erdoğmuş, P., & Yalçın, E. (2015). Parçacık Sürü Optimizasyonu ile Kısıtsız Optimizasyon Test Problemlerinin Çözümü. İleri Teknoloji Bilimleri Dergisi, 4(1), 14–22. https://dergipark.org.tr/tr/pub/duzceitbd/issue/4817/66451
• Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942--1948 c.4. https://doi.org/10.1109/ICNN.1995.488968
• Li, X., Zhao, Z., Liu, L., Liu, Y., & Li, P. (2017). An Optimization Model of Multi-Intersection Signal Control for Trunk Road under Collaborative Information. Journal of Control Science and Engineering, 2017, 2846987. https://doi.org/10.1155/2017/2846987
• Panda, S., & Padhy, N. P. (2007). Comparison of Particle Swarm Optimization and Genetic Algorithm for TCSC-based Controller Design. International Journal of Electrical and Computer Engineering, 1(3), 551–559. https://doi.org/10.5281/ZENODO.1082007
• Parrott, D., & Li, X. (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Transactions on Evolutionary Computation, 10(4), 440–458. https://doi.org/10.1109/TEVC.2005.859468
• Peñabaena-Niebles, R., Cantillo, V., Moura, J. L., & Ibeas, A. (2017). Design and Evaluation of a Mathematical Optimization Model for Traffic Signal Plan Transition Based on Social Cost Function. Journal of Advanced Transportation, 2017, 1943846. https://doi.org/10.1155/2017/1943846
• Shaikh, P. W., El-Abd, M., Khanafer, M., & Gao, K. (2022). A Review on Swarm Intelligence and Evolutionary Algorithms for Solving the Traffic Signal Control Problem. IEEE Transactions on Intelligent Transportation Systems, 23(1), 48–63. https://doi.org/10.1109/TITS.2020.3014296
• Zhang, Y., Gao, K., Zhang, Y., & Su, R. (2019). Traffic Light Scheduling for Pedestrian-Vehicle Mixed-Flow Networks. IEEE Transactions on Intelligent Transportation Systems, 20(4), 1468–1483. https://doi.org/10.1109/TITS.2018.2852646