IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i4p643-d753256.html
   My bibliography  Save this article

Development of Parallel Algorithms for Intelligent Transportation Systems

Author

Listed:
  • Boris Chetverushkin

    (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 125047 Moscow, Russia)

  • Antonina Chechina

    (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 125047 Moscow, Russia)

  • Natalia Churbanova

    (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 125047 Moscow, Russia)

  • Marina Trapeznikova

    (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 125047 Moscow, Russia)

Abstract

This paper deals with the creation of parallel algorithms implementing macro-and microscopic traffic flow models on modern supercomputers. High-performance computing contributes to the development of intelligent transportation systems based on information technologies and aimed at the effective regulation of traffic in large cities. As a macroscopic approach, the quasi-gas-dynamic traffic model approximated by explicit finite-difference schemes is proposed. One- and two-dimensional variants of the system are considered, and the concept of lateral velocity and different equations for obtaining it are discussed. The microscopic approach is represented by the multilane cellular automata model. The previously developed model is extended to reproduce synchronized flow in accordance with Kerner’s three-phase theory. The new version starts from the Kerner–Klenov–Schreckenberg–Wolf model and operates with the concept of the synchronization gap. Macroscopic models are relevant for determining the common characteristics of road traffic, while microscopic models are useful for a detailed description of cars’ movement. Both approaches possess inner parallelism. The parallel algorithms are based on the geometrical parallelism principle with different boundary conditions at interfaces of the subdomains. Sufficiently high speedups were reached when up to 100 processors were involved in calculations. The proposed algorithms can serve as the core of ITS.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:643-:d:753256
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/4/643/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/4/643/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kaur, Ramanpreet & Sharma, Sapna, 2017. "Analysis of driver’s characteristics on a curved road in a lattice model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 59-67.
    2. Kerner, Boris S. & Klenov, Sergey L. & Hermanns, Gerhard & Schreckenberg, Michael, 2013. "Effect of driver over-acceleration on traffic breakdown in three-phase cellular automaton traffic flow models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4083-4105.
    3. Lárraga, M.E. & Alvarez-Icaza, L., 2010. "Cellular automaton model for traffic flow based on safe driving policies and human reactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(23), pages 5425-5438.
    4. 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.
    5. Jie Zhou & Zhong-Ke Shi & Hai-Liang Zhang & Chao-Ping Wang, 2017. "A new lattice model for single-lane traffic flow with the consideration of driver’s memory during a period of time," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(07), pages 1-16, July.
    6. Newell, G. F., 2002. "A simplified car-following theory: a lower order model," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 195-205, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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. Kaur, Daljeet & Sharma, Sapna & Gupta, Arvind Kumar, 2022. "Analyses of lattice hydrodynamic area occupancy model for heterogeneous disorder traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    4. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui, 2020. "Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    5. 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.
    6. 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).
    7. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui & Liu, Feng, 2022. "A data-driven two-lane traffic flow model based on cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    8. Wu, Jinchao & Chen, Bokui & Zhang, Kai & Zhou, Jun & Miao, Lixin, 2018. "Ant pheromone route guidance strategy in intelligent transportation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 591-603.
    9. Huanping Li & Jian Wang & Guopeng Bai & Xiaowei Hu, 2021. "Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads," Energies, MDPI, vol. 14(12), pages 1-21, June.
    10. Gong, Siyuan & Du, Lili, 2018. "Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 25-61.
    11. Saif Eddin Jabari & Laura Wynter, 2016. "Sensor placement with time-to-detection guarantees," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(4), pages 415-433, December.
    12. Xu, Tu & Laval, Jorge, 2020. "Statistical inference for two-regime stochastic car-following models," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 210-228.
    13. Coifman, Benjamin & Ponnu, Balaji, 2020. "Adjacent lane dependencies modulating wave velocity on congested freeways-An empirical study," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 84-99.
    14. Zhang, H.M. & Kim, T., 2005. "A car-following theory for multiphase vehicular traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 39(5), pages 385-399, June.
    15. Daganzo, Carlos F., 2006. "In traffic flow, cellular automata = kinematic waves," Transportation Research Part B: Methodological, Elsevier, vol. 40(5), pages 396-403, June.
    16. Kim, T. & Zhang, H.M., 2008. "A stochastic wave propagation model," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 619-634, August.
    17. Treiber, Martin & Kesting, Arne, 2018. "The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 613-623.
    18. He, Zhengbing & Zheng, Liang & Guan, Wei, 2015. "A simple nonparametric car-following model driven by field data," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 185-201.
    19. Jorge A. Laval & Bhargava R. Chilukuri, 2014. "The Distribution of Congestion on a Class of Stochastic Kinematic Wave Models," Transportation Science, INFORMS, vol. 48(2), pages 217-224, May.
    20. Chen, Danjue & Ahn, Soyoung, 2018. "Capacity-drop at extended bottlenecks: Merge, diverge, and weave," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 1-20.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:643-:d:753256. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.