[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/hin/complx/3515272.html
   My bibliography  Save this article

Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model

Author

Listed:
  • Huiming Duan
  • Xinping Xiao
  • Lingling Pei
Abstract
The traffic-flow system has basic dynamic characteristics. This feature provides a theoretical basis for constructing a reasonable and effective model for the traffic-flow system. The research on short-term traffic-flow forecasting is of wide interest. Its results can be applied directly to advanced traffic information systems and traffic management, providing real-time and effective traffic information. According to the dynamic characteristics of traffic-flow data, this paper extends the mechanical properties, such as distance, acceleration, force combination, and decomposition, to the traffic-flow data vector. According to the mechanical properties of the data, this paper proposes four new models of structural parameters and component parameters, inertia nonhomogenous discrete gray models (referred to as INDGM), and analyzes the important properties of the model. This model examines the construction of the inertia nonhomogenous discrete gray model from the mechanical properties of the data, explaining the classic NDGM modeling mechanism in the meantime. Finally, this paper analyzes the traffic-flow data of Whitemud Drive in Canada and studies the relationship between the inertia model and the traffic-flow state according to the data analysis of the traffic-flow state. A simulation accuracy and prediction accuracy of up to 0.0248 and 0.0273, respectively, are obtained.

Suggested Citation

  • Huiming Duan & Xinping Xiao & Lingling Pei, 2017. "Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model," Complexity, Hindawi, vol. 2017, pages 1-16, July.
  • Handle: RePEc:hin:complx:3515272
    DOI: 10.1155/2017/3515272
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2017/3515272.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2017/3515272.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2017/3515272?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
    ---><---

    References listed on IDEAS

    as
    1. Cheng, Anyu & Jiang, Xiao & Li, Yongfu & Zhang, Chao & Zhu, Hao, 2017. "Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 422-434.
    2. Bouadi, M. & Jetto, K. & Benyoussef, A. & Kenz, A., 2016. "The effect of lateral interaction on traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 76-87.
    3. Chen, Guo & Dong, Zhao Yang & Hill, David J. & Zhang, Guo Hua & Hua, Ke Qian, 2010. "Attack structural vulnerability of power grids: A hybrid approach based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 595-603.
    4. Kerner, Boris S., 2004. "Three-phase traffic theory and highway capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 379-440.
    5. Xing-Wei Ren & Yi-Qun Tang & Jun Li & Qi Yang, 2012. "A prediction method using grey model for cumulative plastic deformation under cyclic loads," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(1), pages 441-457, October.
    6. Wild, Dieter, 1997. "Short-term forecasting based on a transformation and classification of traffic volume time series," International Journal of Forecasting, Elsevier, vol. 13(1), pages 63-72, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    2. Huiming Duan & Xinping Xiao, 2019. "A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors," Complexity, Hindawi, vol. 2019, pages 1-18, June.
    3. Wen-Ze Wu & Tao Zhang & Chengli Zheng, 2019. "A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP," Complexity, Hindawi, vol. 2019, pages 1-10, October.
    4. Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).

    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. Liu, Qingchao & Cai, Yingfeng & Jiang, Haobin & Lu, Jian & Chen, Long, 2018. "Traffic state prediction using ISOMAP manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 532-541.
    2. Huiming Duan & Xinping Xiao, 2019. "A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors," Complexity, Hindawi, vol. 2019, pages 1-18, June.
    3. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.
    4. Su-qi Zhang & Kuo-Ping Lin, 2020. "Short-Term Traffic Flow Forecasting Based on Data-Driven Model," Mathematics, MDPI, vol. 8(2), pages 1-17, January.
    5. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    6. Mohamad Darayi & Kash Barker & Joost R. Santos, 2017. "Component Importance Measures for Multi-Industry Vulnerability of a Freight Transportation Network," Networks and Spatial Economics, Springer, vol. 17(4), pages 1111-1136, December.
    7. Rehan Asad & Muhammad Qaiser Saleem & Muhammad Salman Habib & Nadeem Ahmad Mufti & Shaker Mahmood Mayo, 2023. "Seismic risk assessment and hotspots prioritization: a developing country perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 2863-2901, July.
    8. Simão, Ricardo & Wardil, Lucas, 2021. "Social dilemma in traffic with heterogeneous drivers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    9. Yonghong Hao & Xiang Chen & Xuemeng Wang, 2014. "Investigation of karst hydrological processes by using grey auto-incidence analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1017-1024, March.
    10. Lahmiri, Salim & Bekiros, Stelios & Bezzina, Frank, 2020. "Multi-fluctuation nonlinear patterns of European financial markets based on adaptive filtering with application to family business, green, Islamic, common stocks, and comparison with Bitcoin, NASDAQ, ," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    11. Elena Karnoukhova & Anastasia Stepanova & Maria Kokoreva, 2018. "The Influence Of The Ownership Structure On The Performance Of Innovative Companies In The Us," HSE Working papers WP BRP 70/FE/2018, National Research University Higher School of Economics.
    12. Espejo, Rafael & Lumbreras, Sara & Ramos, Andres, 2018. "Analysis of transmission-power-grid topology and scalability, the European case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 383-395.
    13. Redhu, Poonam & Gupta, Arvind Kumar, 2016. "Effect of forward looking sites on a multi-phase lattice hydrodynamic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 150-160.
    14. Qiang Shang & Yang Yu & Tian Xie, 2022. "A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    15. 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.
    16. Nasiruzzaman, A.B.M. & Pota, H.R. & Akter, Most. Nahida, 2014. "Vulnerability of the large-scale future smart electric power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 11-24.
    17. Xia, Dong & Zheng, Linjiang & Tang, Yi & Cai, Xiaolin & Chen, Li & Sun, Dihua, 2022. "Dynamic traffic prediction for urban road network with the interpretable model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    18. Junwei Zeng & Yongsheng Qian & Fan Yin & Leipeng Zhu & Dejie Xu, 2022. "A multi-value cellular automata model for multi-lane traffic flow under lagrange coordinate," Computational and Mathematical Organization Theory, Springer, vol. 28(2), pages 178-192, June.
    19. Yeo, Hwasoo, 2008. "Asymmetric Microscopic Driving Behavior Theory," University of California Transportation Center, Working Papers qt1tn1m968, University of California Transportation Center.
    20. Wang, Jing & Zuo, Wangda & Rhode-Barbarigos, Landolf & Lu, Xing & Wang, Jianhui & Lin, Yanling, 2019. "Literature review on modeling and simulation of energy infrastructures from a resilience perspective," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 360-373.

    More about this item

    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:hin:complx:3515272. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.