Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions

Mustafa Murat COŞKUN, (Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Ankara, Türkiye)
Cevat ŞENER, (Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Ankara, Türkiye)
İsmail Hakkı TOROSLU (Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Ankara, Türkiye)
Turkish Journal of Electrical Engineering and Computer Sciences
72 0
Yıl: 2024 Cilt: 32 Sayı: 1 Sayfa Aralığı: 1 - 20 Metin Dili: İngilizce DOI: 10.55730/1300-0632.4052 İndeks Tarihi: 14-03-2024

Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions

Öz:
In this study, we present a framework designed to optimize signals at intersections experiencing oversaturated traffic conditions, utilizing mixed-integer linear programming (MILP) techniques. The proposed MILP solutions were developed with different objective functions, namely a reduction in the total remaining queue and fair distribution of the remaining queue after each signal cycle. Our framework contains two distinct stages. The initial stage applies two distinct MILP methodologies, while the subsequent stage employs a neighborhood search method to further reduce the delays associated with the green signal timings derived from the first stage. Ultimately, to evaluate their effectiveness across various intersections, we employed the HCM 2000 delay model for all the models we developed. Our experimental results show that the proposed approach reduces the delay significantly for various intersection designs.
Anahtar Kelime: Mixed-integer linear programming traffic signal optimization signalized intersections oversaturated conditions deterministic queuing

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] Singh L, Tripathi S, Arora H. Time optimization for traffic signal control using genetic algorithm. International Journal of Recent Trends in Engineering 2009; 2 (2): 4.
  • [2] Weisbrod G, Vary D, Treyz G. Economic Implications of Congestion. Technical report, NCHRP REPORT 463, National Cooperative Highway Research Program, Transportation Research Board, 2001.
  • [3] Srisurin P, Singh A. Optimal signal plan for minimizing queue lengths at a congested intersection. International Journal of Traffic and Transport Engineering 2017; 6 (3): 53-63.
  • [4] Liu H, Balke KN, Lin WH. A reverse causal-effect modeling approach for signal control of an oversaturated intersection. Transportation Research Part C: Emerging Technology 2008; 16 (6): 742-754.
  • [5] Srinivasan D, Choy MC, Cheu RL. Neural networks for real-time traffic signal control. IEEE Transactions on Intelligent Transportation Systems 2006; 7 (3): 261-272.
  • [6] Castro GB, Hirakawa AR, Martini JSC. Adaptive traffic signal control based on bio-neural network. Procedia Computer Science 2017; 109: 1182-1187.
  • [7] Trabia MB, Kaseko MS, Ande M. A two-stage fuzzy logic controller for traffic signals. Transportation Research Part C: Emerging Technology 1999; 7 (6): 353-367.
  • [8] Chiu S. Adaptive traffic signal control using fuzzy logic. In: Proceedings of the IEEE Intelligent Vehicles Symposium; Westlake Village, CA, USA; 1992. pp. 98-1007.
  • [9] Zeng J, Hu J, Zhang Y. Adaptive traffic signal control with deep recurrent Q-learning. In: IEEE Intelligent Vehicles Symposium (IV), Proceedings; Changshu, China; 2018. pp. 1215-1220.
  • [10] Liang X, Du X, Wang G, Han Z. Deep reinforcement learning for traffic light control in vehicular networks. IEEE Transactions on Vehicular Technology 2018; 1-11.
  • [11] Shoufeng L, Ximin L, Shiqiang D. Q-learning for adaptive traffic signal control based on delay minimization strategy. In: IEEE International Conference on Networking, Sensing and Control; Sanya, China; 2008. pp. 687-691.
  • [12] Van der Pol E, Oliehoek FA. Coordinated deep reinforcement learners for traffic light control. In: 30th Conference on Neural Information Processing Systems; Barcelona, Spain; 2016.
  • [13] Xiao N, Yu L, Yu J, Chen P, Liu Y. A cold-start-free reinforcement learning approach for traffic signal control. Journal of Intelligent Transportation Systems 2021; 26 (4): 1-10.
  • [14] Park B, Messer CJ, Urbanik T II. Traffic signal optimization program for oversaturated conditions: genetic algorithm approach. Transportation Research Record 1999; 1683 (1): 133-142.
  • [15] Chin YK, Yong KC, Bolong N, Yang SS, Teo KTK. Multiple intersections traffic signal timing optimization with genetic algorithm. In: Proceedings of International Conference on Control System, Computing and Engineering, ICCSCE 2011; Penang, Malaysia; 2011. pp. 454-459.
  • [16] Stevanovic A, Martin PT, Stevanovic J. VisSim-based genetic algorithm optimization of signal timings. Transporta- tion Research Record 2007; 2035 (1): 59-68.
  • [17] Lee J, Abdulhai B, Shalaby A, Chung EH. Real-time optimization for adaptive traffic signal control using genetic algorithms. Journal of Intelligent Transportation Systems 2005; 9 (3): 111-122.
  • [18] Coll P, Factorovich P, Loiseau I, Gomez R. A linear programming approach for adaptive synchronization of traffic signals. International Transactions in Operational Research 2013; 20 (5): 667-679.
  • [19] Wang H, Peng X. Coordinated control model for oversaturated arterial intersections. IEEE Transactions on Intel- ligent Transportation Systems 2022; 23 (12): 24157-24175.
  • [20] Zhang C, Li JY, Chen CH, Li Y, Zhan ZH. Region-based evaluation particle swarm optimization with dual solution libraries for real-time traffic signal timing optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’23; 2023. New York, NY, USA: Association for Computing Machinery. p. 111- 118.
  • [21] Li S, Kang L, Huang H, Liu L. A perimeter control model of urban road network based on cooperative- noncooperative two-stage game. Physica A: Statistical Mechanics and its Applications 2023; 626: 129081.
  • [22] Eom M, Kim BI. The traffic signal control problem for intersections: a review. European Transport Research Review 2020; 12 (1): 50.
  • [23] HCM 2000 Highway Capacity Manual. Technical Report. Washington DC, USA: Transport Research Board, 2000.
  • [24] Shao CQ, Rong J, Liu XM. Study on the saturation flow rate and its influence factors at signalized intersections in China. Procedia - Social and Behavioral Sciences 2011; 16: 504-514.
  • [25] Papageorgiou M, Diakaki C, Dinopoulou V, Kotsialos A, Wang Y. Review of road traffic control strategies. Proceedings of the IEEE 2003; 91 (12): 2043-2065.
  • [26] Fellendorf M, Vortisch P. Microscopic traffic flow simulator VISSIM. In: Barceló J (editor). Fundamentals of Traffic Simulation. International Series in Operations Research & Management Science, vol 145. New York, NY, USA: Springer, 2010, pp. 63-93.
APA Coşkun M, Şener C, toroslu i (2024). Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. , 1 - 20. 10.55730/1300-0632.4052
Chicago Coşkun Mustafa Murat,Şener Cevat,toroslu ismail Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. (2024): 1 - 20. 10.55730/1300-0632.4052
MLA Coşkun Mustafa Murat,Şener Cevat,toroslu ismail Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. , 2024, ss.1 - 20. 10.55730/1300-0632.4052
AMA Coşkun M,Şener C,toroslu i Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. . 2024; 1 - 20. 10.55730/1300-0632.4052
Vancouver Coşkun M,Şener C,toroslu i Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. . 2024; 1 - 20. 10.55730/1300-0632.4052
IEEE Coşkun M,Şener C,toroslu i "Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions." , ss.1 - 20, 2024. 10.55730/1300-0632.4052
ISNAD Coşkun, Mustafa Murat vd. "Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions". (2024), 1-20. https://doi.org/10.55730/1300-0632.4052
APA Coşkun M, Şener C, toroslu i (2024). Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. Turkish Journal of Electrical Engineering and Computer Sciences, 32(1), 1 - 20. 10.55730/1300-0632.4052
Chicago Coşkun Mustafa Murat,Şener Cevat,toroslu ismail Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. Turkish Journal of Electrical Engineering and Computer Sciences 32, no.1 (2024): 1 - 20. 10.55730/1300-0632.4052
MLA Coşkun Mustafa Murat,Şener Cevat,toroslu ismail Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. Turkish Journal of Electrical Engineering and Computer Sciences, vol.32, no.1, 2024, ss.1 - 20. 10.55730/1300-0632.4052
AMA Coşkun M,Şener C,toroslu i Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. Turkish Journal of Electrical Engineering and Computer Sciences. 2024; 32(1): 1 - 20. 10.55730/1300-0632.4052
Vancouver Coşkun M,Şener C,toroslu i Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions. Turkish Journal of Electrical Engineering and Computer Sciences. 2024; 32(1): 1 - 20. 10.55730/1300-0632.4052
IEEE Coşkun M,Şener C,toroslu i "Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions." Turkish Journal of Electrical Engineering and Computer Sciences, 32, ss.1 - 20, 2024. 10.55730/1300-0632.4052
ISNAD Coşkun, Mustafa Murat vd. "Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions". Turkish Journal of Electrical Engineering and Computer Sciences 32/1 (2024), 1-20. https://doi.org/10.55730/1300-0632.4052
Sayfa Oluşturulma Tarihi
24-04-2024 10:55

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