IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v506y2018icp532-541.html
   My bibliography  Save this article

Traffic state prediction using ISOMAP manifold learning

Author

Listed:
  • Liu, Qingchao
  • Cai, Yingfeng
  • Jiang, Haobin
  • Lu, Jian
  • Chen, Long

Abstract

Traffic state prediction is an essential problem with considerable implications in the intelligent transportation system. This paper puts forward an approach for predicting urban road traffic states based on ISOMAP manifold learning. By establishing a distance measurement that represents the overall geometric structure based on the Isometric Feature Mapping (ISOMAP) algorithm, this approach utilizes all consistent information regarding the traffic flow, thus improving the prediction accuracy of the road traffic state. The experimental results indicate that, compared with a traditional prediction approach, the equality coefficient has a bigger increase in value and a much lower prediction error. The traffic state prediction approach based on ISOMAP manifold learning achieves a higher level of accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:532-541
    DOI: 10.1016/j.physa.2018.04.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118304680
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.04.031?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. Hilmi Berk Celikoglu & Mehmet Ali Silgu, 2016. "Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering," Transportation Science, INFORMS, vol. 50(3), pages 966-981, August.
    2. 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.
    3. 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.
    4. Zhu, Wen-Xing & Zhang, H.M., 2018. "Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 274-285.
    5. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Modeling connected and autonomous vehicles in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 269-277.
    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, Jiaxin & Zhou, Xubing & Peng, Yi & Zhao, Xiaojun, 2022. "Recurrence analysis of urban traffic congestion index on multi-scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    2. Xuguang Han & Jingming Su & Yan Hong & Pingshun Gong & Danping Zhu, 2022. "Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    3. Wenhao Li & Chengkun Liu & Tao Wang & Yanjie Ji, 2024. "An innovative supervised learning structure for trajectory reconstruction of sparse LPR data," Transportation, Springer, vol. 51(1), pages 73-97, February.
    4. Huang, Yan & Wan, Jiansong & Huang, Xin, 2019. "Quantitative analysis of financial system fragility based on manifold curvature," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1276-1285.

    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. 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.
    2. Wang, Shutong & Zhu, Wen-Xing, 2022. "Modeling the heterogeneous traffic flow considering mean expected velocity field and effect of two-lane communication under connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. Tanimoto, Jun & Futamata, Masanori & Tanaka, Masaki, 2020. "Automated vehicle control systems need to solve social dilemmas to be disseminated," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Peng, Jiali & Shangguan, Wei & Peng, Cong & Chai, Linguo, 2024. "Uncertainty modeling of connected and automated vehicle penetration rate under mixed traffic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    5. Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    6. Gu, Yewen & Goez, Julio C. & Mario, Guajardo & Wallace, Stein W., 2019. "Autonomous vessels: State of the art and potential opportunities in logistics," Discussion Papers 2019/6, Norwegian School of Economics, Department of Business and Management Science.
    7. Jin, Shuang & Sun, Di-Hua & Zhao, Min & Li, Yang & Chen, Jin, 2020. "Modeling and stability analysis of mixed traffic with conventional and connected automated vehicles from cyber physical perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    8. Luo, Ruifa & Gu, Qiufan & Xu, Taorang & Hao, Huijun & Yao, Zhihong, 2022. "Analysis of linear internal stability for mixed traffic flow of connected and automated vehicles considering multiple influencing factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    9. 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.
    10. Pernestål Brenden , Anna & Kristoffersson , Ida, 2018. "Effects of driverless vehicles: A review of simulations," Working papers in Transport Economics 2018:11, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    11. 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).
    12. Pedro Cesar Lopes Gerum & Andrew Reed Benton & Melike Baykal-Gürsoy, 2019. "Traffic density on corridors subject to incidents: models for long-term congestion management," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 795-831, December.
    13. Wang, Xiaoning & Liu, Minzhuang & Ci, Yusheng & Wu, Lina, 2022. "Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    14. Tang, Tie-Qiao & Shi, Wei-Fang & Huang, Hai-Jun & Wu, Wen-Xiang & Song, Ziqi, 2019. "A route-based traffic flow model accounting for interruption factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 767-785.
    15. Hossain, Md. Anowar & Tanimoto, Jun, 2022. "A microscopic traffic flow model for sharing information from a vehicle to vehicle by considering system time delay effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    16. 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).
    17. 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.
    18. Andrea Papu Carrone & Jeppe Rich & Christian Anker Vandet & Kun An, 2021. "Autonomous vehicles in mixed motorway traffic: capacity utilisation, impact and policy implications," Transportation, Springer, vol. 48(6), pages 2907-2938, December.
    19. Zong, Fang & Wang, Meng & Tang, Jinjun & Zeng, Meng, 2022. "Modeling AVs & RVs’ car-following behavior by considering impacts of multiple surrounding vehicles and driving characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    20. Yan, Chunyue & Ge, Hongxia & Cheng, Rongjun, 2019. "An extended car-following model by considering the optimal velocity difference and electronic throttle angle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

    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:phsmap:v:506:y:2018:i:c:p:532-541. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.