IDEAS home Printed from https://ideas.repec.org/a/ags/ndjtrf/317645.html
   My bibliography  Save this article

Prediction of Freeway Traffic Flows Using Kalman Predictor in Combination With Time Series

Author

Listed:
  • Jiang, Yi

Abstract

No abstract is available for this item.

Suggested Citation

  • Jiang, Yi, 2003. "Prediction of Freeway Traffic Flows Using Kalman Predictor in Combination With Time Series," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 42(2).
    • Handle: RePEc:ags:ndjtrf:317645
    DOI: 10.22004/ag.econ.317645
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/317645/files/agecon-jtrf-0476.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.317645?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. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    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. Huanyin Su & Shanglin Mo & Shuting Peng, 2023. "Short-Term Prediction of Time-Varying Passenger Flow for Intercity High-Speed Railways: A Neural Network Model Based on Multi-Source Data," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    2. Xing, Tao & Zhou, Xuesong & Taylor, Jeffrey, 2013. "Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 66-90.
    3. Wei Zhou & Wei Wang & Xuedong Hua & Yi Zhang, 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
    4. Cantelmo, Guido & Qurashi, Moeid & Prakash, A. Arun & Antoniou, Constantinos & Viti, Francesco, 2020. "Incorporating trip chaining within online demand estimation," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 171-187.
    5. Lederman, Roger & Wynter, Laura, 2011. "Real-time traffic estimation using data expansion," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1062-1079, August.
    6. M. Bierlaire & F. Crittin, 2004. "An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables," Operations Research, INFORMS, vol. 52(1), pages 116-127, February.
    7. Zhou, Xuesong & Mahmassani, Hani S., 2007. "A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework," Transportation Research Part B: Methodological, Elsevier, vol. 41(8), pages 823-840, October.
    8. David Watling & Giulio Cantarella, 2015. "Model Representation & Decision-Making in an Ever-Changing World: The Role of Stochastic Process Models of Transportation Systems," Networks and Spatial Economics, Springer, vol. 15(3), pages 843-882, September.
    9. Cai, Lingru & Zhang, Zhanchang & Yang, Junjie & Yu, Yidan & Zhou, Teng & Qin, Jing, 2019. "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    10. Jiasong Zhu & Anthony Gar-On Yeh, 2012. "A Self-Learning Short-Term Traffic Forecasting System," Environment and Planning B, , vol. 39(3), pages 471-485, June.
    11. Safikhani, Abolfazl & Kamga, Camille & Mudigonda, Sandeep & Faghih, Sabiheh Sadat & Moghimi, Bahman, 2020. "Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1138-1148.
    12. Hu, Guojing & Whalin, Robert W. & Kwembe, Tor A. & Lu, Weike, 2023. "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    13. Zhang, Jie & Song, Chunyue & Cao, Shan & Zhang, Chun, 2023. "FDST-GCN: A Fundamental Diagram based Spatiotemporal Graph Convolutional Network for expressway traffic forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    14. Simonelli, Fulvio & Marzano, Vittorio & Papola, Andrea & Vitiello, Iolanda, 2012. "A network sensor location procedure accounting for o–d matrix estimate variability," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1624-1638.
    15. Balaji Ganesh Rajagopal & Manish Kumar & Pijush Samui & Mosbeh R. Kaloop & Usama Elrawy Shahdah, 2022. "A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    16. Hongxia Ge & Siteng Li & Rongjun Cheng & Zhenlei Chen, 2022. "Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    17. Lu, Xijin & Ma, Changxi & Qiao, Yihuan, 2021. "Short-term demand forecasting for online car-hailing using ConvLSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    18. He, Yuxin & Zhao, Yang & Luo, Qin & Tsui, Kwok-Leung, 2022. "Forecasting nationwide passenger flows at city-level via a spatiotemporal deep learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    19. Guo, Jianhua & Liu, Yu & Li, Xiugang & Huang, Wei & Cao, Jinde & Wei, Yun, 2019. "Enhanced least square based dynamic OD matrix estimation using Radio Frequency Identification data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 27-40.
    20. Ximan Ling & Zhiren Huang & Chengcheng Wang & Fan Zhang & Pu Wang, 2018. "Predicting subway passenger flows under different traffic conditions," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.

    More about this item

    Keywords

    Public Economics;

    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:ags:ndjtrf:317645. 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: AgEcon Search (email available below). General contact details of provider: http://www.trforum.org/journal/ .

    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.