Traffic Simulation of a Signalized Intersection During Rush Hours: A Case Study

Yıl 2024, Cilt: 35 Sayı: 2, 136 - 166, 01.09.2024

Sinem Özkan , Mert Paldrak , Erdinç Öner

https://doi.org/10.46465/endustrimuhendisligi.1398935

Öz

This article focuses on simulating the traffic of one of the most crowded signalized intersections, Vakıflar Intersection, in Izmir during rush hours. The main goals of this study are examining the bottlenecks in the intersection and overcome the bottlenecks with proposing alternative solutions. A simulation model is generated and the results show that a significant number of vehicles are waiting in the eastbound and westbound directions of the intersection. To solve the problem, a new model with an underpass connecting the eastbound and westbound directions of the intersection is proposed. The results attained from the developed model have shown that the waiting time of the vehicles and the number of vehicles waiting in the queue in Şehitler Street and Kamil Tunca Boulevard have dramatically decreased.

Anahtar Kelimeler

Traffic Signal, Simulation, Underpasses, Roundabouts

Yoğun Saatlerde Sinyalize Bir Kavşağın Trafik Simülasyonu: Bir Vaka Çalışması

Öz

Bu çalışmada, İzmir'in en yoğun sinyalize kavşaklarından biri olan Vakıflar Kavşağının, trafiğin yoğun saatlerde simüle edilmesine odaklanılmıştır. Çalışma, kavşaktaki darboğazları daha iyi anlamak ve analiz etmek ve iyileştirmek için çözümler önermek amacıyla ağ üzerindeki trafiği simüle etmek amacıyla yürütülmüştür. Simülasyon modeli ARENA Yazılımında oluşturulmuş ve ilk sonuçlar kavşağın doğu ve batı güzergahlarında önemli bir kuyruk problemi olduğunu göstermiştir. Sorunun üstesinden gelmek için kavşağın doğu ve batı taraflarını birbirine bağlayan bir alt geçit içeren yeni bir tasarım önerilmiştir. Geliştirilen modelden elde edilen sonuçlar, Şehitler Caddesi ve Kamil Tunca Bulvarı'nda kuyrukta bekleyen araç sayısının ve bekleme süresinin önemli ölçüde azaldığını göstermiştir.

Anahtar Kelimeler

Trafik Sinyali, Simülasyon, Alt Geçitler, Dönel Kavşaklar

Kaynakça

• Abdelghaffar, H. M., Yang, H., & Rakha, H. A. (2017). Developing a de-centralized cycle-free nash bargaining arterial traffic signal controller. 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (pp. 544-549). Doi: https://doi.org/10.1109/MTITS.2017. 8005732
• Akcelik, R. (1981). Traffic signals: Capacity and timing analysis. Melbourne: Australian Road Research Board, ARR.
• Allsop, R. E. (1972). Delay at a fixed time traffic signal—I: Theoretical analysis. Transportation Science, 6(3), 260-285. Doi: https://doi.org/10.1287/trsc.6.3.260
• Araghi, S., Khosravi, A., & Creighton, D. (2015). Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network. Expert Systems with Applications, 42(9), 4422-4431. Doi: https://doi.org/10.1016/j.eswa.2015.01.063
• Carson, Y., & Maria, A. (1997, December). Simulation optimization: methods and applications. In Proceedings of the 29th conference on Winter simulation (pp. 118-126). Doi: https://doi.org/10.1145/268437.268460
• Chen, S., & Sun, D. J. (2016). An improved adaptive signal control method for isolated signalized intersection based on dynamic programming. IEEE Intelligent Transportation Systems Magazine, 8(4), 4-14. Doi: https://doi.org/10.1109/MITS.2016.2605318
• Dabiri, S., & Abbas, M. (2016, November). Arterial traffic signal optimization using particle swarm optimization in an integrated VISSIM-MATLAB simulation environment. IEEE 19th international conference on intelligent transportation systems (pp. 766-771). IEEE. Doi: https://doi.org/10.1109/ITSC.2016.7795641
• Eiben, A. E., & Smith, J. E. (2015). Introduction to evolutionary computing. Berlin: Springer.
• Gökçe, M. A., Öner, E., & Işık, G. (2015). Traffic signal optimization with particle swarm optimization for signalized roundabouts. Simulation, 91(5), 456-466. Doi: https://doi.org/10.1177/0037549715581473
• Hajbabaie, A., & Benekohal, R. F. (2015). A program for simultaneous network signal timing optimization and traffic assignment. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2573-2586. Doi: https://doi.org/10.1109/TITS.2015.2413360
• Jin, J., Ma, X., & Kosonen, I. (2017). An intelligent control system for traffic lights with simulation-based evaluation. Control engineering practice, 58, 24-33. Doi: https://doi.org/10.1016/j.conengprac.2016.09.009
• Köhler, E., & Strehler, M. (2019). Traffic signal optimization: Combining static and dynamic models. Transportation science, 53(1), 21-41. Doi: https://doi.org/10.1287/trsc.2017.0760
• Li, Z., & Schonfeld, P. (2015). Hybrid simulated annealing and genetic algorithm for optimizing arterial signal timings under oversaturated traffic conditions. Journal of advanced transportation, 49(1), 153-170. Doi: https://doi.org/10.1002/atr.1274
• Louati, A., Elkosantini, S., Darmoul, S., & Ben Said, L. (2019). An immune memory inspired case-based reasoning system to control interrupted flow at a signalized intersection. Artificial Intelligence Review, 52, 2099-2129. Doi: https://doi.org/10.1007/s10462-017-9604-0
• Miletić, M., Kapusta, B., & Ivanjko, E. (2018, September). Comparison of two approaches for preemptive traffic light control. In 2018 international symposium ELMAR (pp. 57-62). IEEE. Doi: https://doi.org/10.23919/ELMAR.2018.8534608
• Mok, K., & Zhang, L. (2024). Adaptive traffic signal management method combining deep learning and simulation. Multimedia Tools and Applications, 83(5), 15439-15459. Doi: https://doi.org/10.1007/s11042-022-13033-5
• Murat, Y. S., Cakici, Z., & Tian, Z. (2019). A signal timing assignment proposal for urban multi lane signalised roundabouts. Građevinar, 71(02.), 113-124. Doi: https://doi.org/10.14256/JCE.2323.2018
• Nguyen, P. T. M., Passow, B. N., & Yang, Y. (2016, July). Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 4611-4618). IEEE. Doi: https://doi.org/10.1109/IJCNN.2016.7727804
• Otamendi, J., Pastor, J. M., & Garcı, A. (2008). Selection of the simulation software for the management of the operations at an international airport. Simulation Modelling Practice and Theory, 16(8), 1103-1112. Doi: https://doi.org/10.1016/j.simpat.2008.04.022
• Qadri, S. S. S. M., Gökçe, M. A., & Öner, E. (2020). State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review, 12, 1-23. Doi: https://doi.org/10.1186/s12544-020-00439-1
• Robertson, D.I., 1969. TRANSYT: a traffic network study tool. UK: Crowthorne
• Sheu, J. B. (2006). A composite traffic flow modeling approach for incident-responsive network traffic assignment. Physica A: Statistical Mechanics and its Applications, 367, 461-478. Doi: https://doi.org/10.1016/ j.physa.2005.11.039
• Spall, J. C., & Chin, D. C. (1997). Traffic-responsive signal timing for system-wide traffic control. Transportation Research Part C: Emerging Technologies, 5(3-4), 153-163. Doi: https://doi.org/10.1016/S0968-090X(97)00012-0
• Stupin, A., Kazakovtsev, L., & Stupina, A. (2022). Control of traffic congestion by improving the rings and optimizing the phase lengths of traffic lights with the help of anylogic. Transportation research procedia, 63, 1104-1113. Doi: https://doi.org/10.1016/j.trpro.2022.06.113
• Tang, C., Xia, S., Zhu, C., & Wei, X. (2019). Phase timing optimization for smart traffic control based on fog computing. IEEE Access, 7, 84217-84228. Doi: https://doi.org/10.1109/ACCESS.2019.2925134
• Van Woensel, T., & Vandaele, N. (2007). Modeling traffic flows with queueing models: a review. Asia-Pacific Journal of Operational Research, 24(04), 435-461. Doi: https://doi.org/10.1142/S0217595907001383
• Venayagamoorthy, G. K. K. (2009). A successful interdisciplinary course on coputational intelligence. IEEE Computational Intelligence Magazine, 4(1), 14-23. Doi: https://doi.org/10.1109/MCI.2008.930983
• Webster, F. V. (1958). Traffic signal settings. Road Research Laboratory, London, U.K., Road Res. Tech. Paper no. 39. Retrieved from https://trid.trb.org/View/113579
• Zhao, D., Dai, Y., & Zhang, Z. (2011). Computational intelligence in urban traffic signal control: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 485-494. Doi: https://doi.org/10.1109/TSMCC.2011.2161577