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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
30.04.2024
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Copyright (c) 2024 Hongluan Zhao, Mengmeng Su

Traffic Signal Timing Scheme Based on the Improved Harris Hawks Optimisation

Authors:Hongluan Zhao, Mengmeng Su

Abstract

With the continuous increase of urban vehicles, traffic congestion becomes severe in the metropolitan areas and higher car utilisation areas. The traffic signal timing scheme can effectively alleviate traffic congestion at intersections. We need to make a profound study in the traffic signal timing. An optimisation model is established, which not only takes the average delay time of vehicles, the number of vehicle stops and the traffic capacity, but also takes the exhaust emissions as the evaluation indexes. The model is too complex and involves too many variables to be solved by using multi-objective programming. Thus, the Harris Hawks Optimisation (HHO) with few parameters and high search accuracy was used to solve the model. To avoid the disadvantages of poor search performance and easy to fall into local optimisation of the Harris Hawks Algorithm, multi-strategy improvements were introduced. The experimental effects show that during the peak hours of traffic flow, the improved algorithm can reduce the average vehicle delay by 36.7%, the exhaust emission by 31.2% and increase the vehicle capacity by 41.6%. The above indicators have also been upgraded during the low peak stage.

Keywords:urban traffic control, traffic optimisation, signalised intersection, Harris Hawks Optimisation

References

  1. [1] Yau KLA, et al. A survey on reinforcement learning models and algorithms for traffic signal control. ACM Computing Surveys. 2017;50(3):1-38. DOI: 10.1145/3068287.
  2. [2] Rouphail NM, et al. Vehicle emissions and traffic measures: Exploratory analysis of field observations at signalized arterials. 80th Annual Meeting of the Transportation Research Board, Washington, DC. 2001.
  3. [3] Tajalli M, Hajbabaie A. Traffic signal timing and trajectory optimization in a mixed autonomy traffic stream. IEEE Transactions on Intelligent Transportation Systems. 2021;23(7):6525-6538. DOI: 10.1109/TITS.2021.3058193.
  4. [4] Webster FV, Cobbe BM. Traffic signals research report. London: Road Research Laboratory, 1966.
  5. [5] Park B, Messer CJ, Urbanik T. Enhanced genetic algorithm for signal-timing optimization of oversaturated intersections. Transportation Research Record. 2000;1727(1):32-41. DOI: 10.3141/1727-05.
  6. [6] Chin YK, et al. Multiple intersections traffic signal timing optimization with genetic algorithm. 2011 IEEE International Conference on Control System, Computing and Engineering. IEEE; 2011. p. 454-459. DOI: 10.1109/ICCSCE.2011.6190569.
  7. [7] Papatzikou E, Stathopoulos A. Rapid algorithm for finding the best combination of signaling phases using optimization methods. International Journal of Transportation Science and Technology. 2018;7(4):229-240. DOI: 10.1016/j.ijtst.2018.10.005.
  8. [8] Mercader P, et al. Optimal signal timing for multi-phase intersections. IFAC-Papers On Line. 2018;51(9):476-481. DOI: 10.1016/j.ifacol.2018.07.078.
  9. [9] Ceylan H, Bell MG. Traffic signal timing optimization based on genetic algorithm approach, including drivers’ routing. Transportation Research Part B: Methodological. 2004;38(4):329-342. DOI: 10.1016/S0191-2615(03)00015-8.
  10. [10] Chentoufi MA, Ellaia RA. Hybrid particle swarm optimization and tabu search algorithm for adaptive traffic signal timing optimization. 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD). IEEE. 2018. p. 25-30. DOI: 10.1109/ITMC.2018.8691188.
  11. [11] Liu CY, Ren YY, Bi XJ. Timing optimization of regional traffic signals based on improved firefly algorithm. Control and Decision. 2020;35(12):2829-2834.
  12. [12] Hawash A, et al. Whale optimization algorithm for traffic signal scheduling problem. International Conference on Innovative Computing and Cutting-edge Technologies. 2019. DOI: 10.1007/978-3-030-38501-9_17.
  13. [13] Zhao ZT. A real-time adaptive signal timing optimization method based on improved grey wolf algorithm. Patent. open.
  14. [14] Qiao Z, et al. Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy. Applied Intelligence. 2021;51:1-17. DOI: 10.1007/s10489-021-02256-y.
  15. [15] Heidari AA, et al. Harris Hawks optimization: Algorithm and applications. Future Generation Computer Systems. 2019;97:849-872. DOI: 10.1016/j.future.2019.02.028.
  16. [16] HC manual. Transportation research board. Nation Research Council, Washington, DC. 2000. 113:10.
  17. [17] Akçelik R, Rouphail NM. Estimation of delays at traffic signals for variable demand conditions. Transportation Research Part B: Methodological. 1993;27(2):109-131. DOI: 10.1016/0191-2615(93)90003-S.
  18. [18] Tizhoosh HR. Opposition-based learning: A new scheme for machine intelligence. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce. IEEE; 2005. p. 695-701. DOI: 10.1109/CIMCA.2005.1631345.
  19. [19] Zhang S, Luo Q, Zhou Y. Hybrid gray wolf optimizer using elite opposition-based learning strategy and simplex method. International Journal of Computational Intelligence and Applications. 2017;16(02):1750012. DOI: 10.1142/S1469026817500122.
  20. [20] Tanyildizi E, Demir G. Golden sine algorithm: A novel math-inspired algorithm. Advances in Electrical and Computer Engineering. 2017;17(2):71-78. DOI: 10.4316/AECE.2017.02010.
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