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A fast simulation model for traffic flow on the basis of boolean operations

Author

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  • Cremer, M.
  • Ludwig, J.

Abstract

A fast simulation model for the dynamic process of traffic flow through urban networks is presented. The model simulates the progression of cars on a street by moving 1 bit variables through binary positions of bytes in the storage which are arranged to copy the topology of a specified network. Skillful application of boolean operations enable the model to perform diverse movements of a vehicle like driving at a constant speed, lane changing, passing, decelerating and accelerating, queueing and turning at intersections. The model simulates accurately macroscopic phenomena of traffic flow while at the same time reproducing the main mechanisms of microscopic models. The computational requirements are rather low with respect to both storage and computation time making it possible to simulate large traffic networks on personal computers.

Suggested Citation

  • Cremer, M. & Ludwig, J., 1986. "A fast simulation model for traffic flow on the basis of boolean operations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 28(4), pages 297-303.
    • Handle: RePEc:eee:matcom:v:28:y:1986:i:4:p:297-303
    DOI: 10.1016/0378-4754(86)90051-0
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    Cited by:

    1. Minh Sang Pham Do & Ketoma Vix Kemanji & Man Dinh Vinh Nguyen & Tuan Anh Vu & Gerrit Meixner, 2023. "The Action Point Angle of Sight: A Traffic Generation Method for Driving Simulation, as a Small Step to Safe, Sustainable and Smart Cities," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    2. Sun, Lu & Jafaripournimchahi, Ammar & Hu, Wusheng, 2020. "A forward-looking anticipative viscous high-order continuum model considering two leading vehicles for traffic flow through wireless V2X communication in autonomous and connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    3. Sun, Yi, 2020. "Kinetic Monte Carlo simulations of bi-direction pedestrian flow with different walk speeds," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    4. Boris Chetverushkin & Antonina Chechina & Natalia Churbanova & Marina Trapeznikova, 2022. "Development of Parallel Algorithms for Intelligent Transportation Systems," Mathematics, MDPI, vol. 10(4), pages 1-18, February.
    5. Ziwen Song & Feng Sun & Rongji Zhang & Yingcui Du & Guiliang Zhou, 2021. "An Improved Cellular Automaton Traffic Model Based on STCA Model Considering Variable Direction Lanes in I-VICS," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    6. Dewen Kong & Xiucheng Guo & Bo Yang & Dingxin Wu, 2016. "Analyzing the Impact of Trucks on Traffic Flow Based on an Improved Cellular Automaton Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-14, September.
    7. Бекларян Л.А.* & Хачатрян Н.К.**, 2019. "Динамические Модели Организации Грузопотока На Железнодорожном Транспорте," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(3), pages 62-73, июль.
    8. Chen, Yanyan & Chen, Ning & Wang, Yang & Wang, Zhenbao & Feng, Guochen, 2015. "Modeling pedestrian behaviors under attracting incidents using cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 287-300.
    9. Sun, Yi, 2018. "Kinetic Monte Carlo simulations of two-dimensional pedestrian flow models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 836-847.
    10. Yang, Da & Qiu, Xiaoping & Yu, Dan & Sun, Ruoxiao & Pu, Yun, 2015. "A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 62-72.
    11. Sun, Yi, 2019. "Simulations of bi-direction pedestrian flow using kinetic Monte Carlo methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 519-531.
    12. Tianjun Feng & Keyi Liu & Chunyan Liang, 2023. "An Improved Cellular Automata Traffic Flow Model Considering Driving Styles," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    13. Kai Nagel & Peter Wagner & Richard Woesler, 2003. "Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling," Operations Research, INFORMS, vol. 51(5), pages 681-710, October.
    14. Lu Liu & Zhanglei Bian & Qinghui Nie, 2022. "Finding the Optimal Bus-Overtaking Rules for Bus Stops with Two Tandem Berths," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    15. Jing, Dian & Yao, Enjian & Chen, Rongsheng, 2023. "Moving characteristics analysis of mixed traffic flow of CAVs and HVs around accident zones," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    16. Liu, Keyi & Feng, Tianjun, 2023. "Heterogeneous traffic flow cellular automata model mixed with intelligent controlled vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    17. Gao, Kun & Jiang, Rui & Wang, Bing-Hong & Wu, Qing-Song, 2009. "Discontinuous transition from free flow to synchronized flow induced by short-range interaction between vehicles in a three-phase traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(15), pages 3233-3243.
    18. Xiaoyuan Wang & Junyan Han & Chenglin Bai & Huili Shi & Jinglei Zhang & Gang Wang, 2021. "Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment," Future Internet, MDPI, vol. 13(4), pages 1-17, March.

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