IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v181y2024ics1366554523003563.html
   My bibliography  Save this article

A parallel computing framework for large-scale microscopic traffic simulation based on spectral partitioning

Author

Listed:
  • Liu, Zhiyuan
  • Xie, Shen
  • Zhang, Honggang
  • Zhou, Dinghao
  • Yang, Yuwei

Abstract

This paper introduces a parallel computing framework based on the Spectral Partitioning (SP) method designed to enhance the computational efficiency of large-scale microscopic traffic simulation (LSMTS). The framework employs the SP method to partition road networks, taking into account vehicle information and road information as constitutive components for node weight determination. Micro-simulation relies on vehicle information from both preceding and following vehicles to accurately infer the operational states of a vehicle. However, network partitioning can disrupt the flow of vehicle information, resulting in its loss. To address this, the proposed framework incorporates a boundary transmission method to ensure simulation accuracy and precision. This study presents an improved SP (iSP) method tailored for LSMTS, further enhancing the partitioning results achieved through the SP method. Lastly, the framework's validity is confirmed through road network experiments of varying scales and densities, with comparisons made to existing parallel simulation methods. The results demonstrate that the framework significantly reduces the execution time of simulation tasks while maintaining a high level of load balance and minimizing communication overhead.

Suggested Citation

  • Liu, Zhiyuan & Xie, Shen & Zhang, Honggang & Zhou, Dinghao & Yang, Yuwei, 2024. "A parallel computing framework for large-scale microscopic traffic simulation based on spectral partitioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:transe:v:181:y:2024:i:c:s1366554523003563
    DOI: 10.1016/j.tre.2023.103368
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554523003563
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2023.103368?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cheng, Qixiu & Liu, Zhiyuan & Lin, Yuqian & Zhou, Xuesong (Simon), 2021. "An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 246-271.
    2. Jianlin Jiang & Liyun Ling & Yan Gu & Su Zhang & Yibing Lv, 2023. "Customized Alternating Direction Methods of Multipliers for Generalized Multi-facility Weber Problem," Journal of Optimization Theory and Applications, Springer, vol. 196(1), pages 362-389, January.
    3. Liu, Zhiyuan & Zhang, Honggang & Zhang, Kai & Zhou, Zihan, 2023. "Integrating alternating direction method of multipliers and bush for solving the traffic assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    4. Huo, Jinbiao & Liu, Zhiyuan & Chen, Jingxu & Cheng, Qixiu & Meng, Qiang, 2023. "Bayesian optimization for congestion pricing problems: A general framework and its instability," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 1-28.
    5. Liu, Zhiyuan & Chen, Xinyuan & Hu, Jintao & Wang, Shuaian & Zhang, Kai & Zhang, Honggang, 2023. "An alternating direction method of multipliers for solving user equilibrium problem," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1072-1084.
    6. Zhan, Xingbin & Szeto, W.Y. & (Michael) Chen, Xiqun, 2022. "A simulation–optimization framework for a dynamic electric ride-hailing sharing problem with a novel charging strategy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    7. Yu-Hong Dai & Fangfang Xu & Liwei Zhang, 2023. "Alternating direction method of multipliers for linear hyperspectral unmixing," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 97(3), pages 289-310, June.
    8. Ji, Yuxuan & Geroliminis, Nikolas, 2012. "On the spatial partitioning of urban transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1639-1656.
    9. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    10. Bell, Michael G.H. & Kurauchi, Fumitaka & Perera, Supun & Wong, Walter, 2017. "Investigating transport network vulnerability by capacity weighted spectral analysis," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 251-266.
    11. Qi, Mingyao & Lin, Wei-Hua & Li, Nan & Miao, Lixin, 2012. "A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 248-257.
    12. Ronghui Liu, 2010. "Traffic Simulation with DRACULA," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 295-322, Springer.
    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. Cheng, Qixiu & Lin, Yuqian & Zhou, Xuesong (Simon) & Liu, Zhiyuan, 2024. "Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters," European Journal of Operational Research, Elsevier, vol. 312(1), pages 182-197.
    2. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    3. Zhang, Honggang & Liu, Zhiyuan & Wang, Jian & Wu, Yunchi, 2023. "A novel flow update policy in solving traffic assignment problems: Successive over relaxation iteration method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    4. Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 193-211.
    5. Zhang, Yuan & Li, Lu & Zhang, Wenbo & Cheng, Qixiu, 2022. "GATC and DeepCut: Deep spatiotemporal feature extraction and clustering for large-scale transportation network partition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    6. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    7. Zhang, Lele & Garoni, Timothy M & de Gier, Jan, 2013. "A comparative study of Macroscopic Fundamental Diagrams of arterial road networks governed by adaptive traffic signal systems," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 1-23.
    8. Dantsuji, Takao & Sugishita, Kashin & Fukuda, Daisuke, 2023. "Understanding changes in travel patterns during the COVID-19 outbreak in the three major metropolitan areas of Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    9. Dantsuji, Takao & Takayama, Yuki & Fukuda, Daisuke, 2023. "Perimeter control in a mixed bimodal bathtub model," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 267-291.
    10. Karakose, Gokhan & McGarvey, Ronald G., 2018. "Capacitated path-aggregation constraint model for arc disruption in networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 225-238.
    11. Takao Dantsuji & Daisuke Fukuda & Nan Zheng, 2021. "Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach," Transportation, Springer, vol. 48(2), pages 673-697, April.
    12. Du, Jie & Wong, S.C. & Shu, Chi-Wang & Xiong, Tao & Zhang, Mengping & Choi, Keechoo, 2013. "Revisiting Jiang’s dynamic continuum model for urban cities," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 96-119.
    13. Yin, Ruyang & Zheng, Nan & Liu, Zhiyuan, 2022. "Estimating fundamental diagram for multi-modal signalized urban links with limited probe data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    14. Kouvelas, Anastasios & Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Enhancing model-based feedback perimeter control with data-driven online adaptive optimization," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 26-45.
    15. Zhao, De & Ong, Ghim Ping, 2021. "Geo-fenced parking spaces identification for free-floating bicycle sharing system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 49-63.
    16. Sun, Chenshuo & Pei, Xin & Hao, Junheng & Wang, Yewen & Zhang, Zuo & Wong, S.C., 2018. "Role of road network features in the evaluation of incident impacts on urban traffic mobility," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 101-116.
    17. Lu, Qing-Chang & Zhang, Lei & Xu, Peng-Cheng & Cui, Xin & Li, Jing, 2022. "Modeling network vulnerability of urban rail transit under cascading failures: A Coupled Map Lattices approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    18. Leclercq, Ludovic & Ladino, Andres & Becarie, Cécile, 2021. "Enforcing optimal routing through dynamic avoidance maps," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 118-137.
    19. Sugiura, Satoshi & Chen, Anthony, 2021. "Vulnerability analysis of cut-capacity structure and OD demand using Gomory-Hu tree method," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 111-127.
    20. Kashin Sugishita & Yasuo Asakura, 2021. "Vulnerability studies in the fields of transportation and complex networks: a citation network analysis," Public Transport, Springer, vol. 13(1), pages 1-34, March.

    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:eee:transe:v:181:y:2024:i:c:s1366554523003563. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.