IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v13y1997i1p63-72.html
   My bibliography  Save this article

Short-term forecasting based on a transformation and classification of traffic volume time series

Author

Listed:
  • Wild, Dieter

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:intfor:v:13:y:1997:i:1:p:63-72
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169-2070(96)00701-7
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. 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)

    Citations

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


    Cited by:

    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. Lunacek, Monte & Williams, Lindy & Severino, Joseph & Ficenec, Karen & Ugirumurera, Juliette & Eash, Matthew & Ge, Yanbo & Phillips, Caleb, 2021. "A data-driven operational model for traffic at the Dallas Fort Worth International Airport," Journal of Air Transport Management, Elsevier, vol. 94(C).
    3. Ciyun Lin & Kang Wang & Dayong Wu & Bowen Gong, 2020. "Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study," Sustainability, MDPI, vol. 12(17), pages 1-22, August.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. Shenghan Zhou & Chaofan Wei & Chaofei Song & Yu Fu & Rui Luo & Wenbing Chang & Linchao Yang, 2022. "A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    7. Zhai, Linbo & Yang, Yong & Song, Shudian & Ma, Shuyue & Zhu, Xiumin & Yang, Feng, 2021. "Self-supervision Spatiotemporal Part-Whole Convolutional Neural Network for Traffic Prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    8. Owais, Mahmoud & Moussa, Ghada S. & Hussain, Khaled F., 2019. "Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach," Operations Research Perspectives, Elsevier, vol. 6(C).
    9. 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).
    10. Lu, Wenqi & Yi, Ziwei & Wu, Renfei & Rui, Yikang & Ran, Bin, 2022. "Traffic speed forecasting for urban roads: A deep ensemble neural network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    11. 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.
    12. 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.
    13. 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.
    14. Zhao, Jiandong & Yu, Zhixin & Yang, Xin & Gao, Ziyou & Liu, Wenhui, 2022. "Short term traffic flow prediction of expressway service area based on STL-OMS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    15. Wang, Wei & Zhang, Hanyu & Li, Tong & Guo, Jianhua & Huang, Wei & Wei, Yun & Cao, Jinde, 2020. "An interpretable model for short term traffic flow prediction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 264-278.
    16. Min Li & Mengshan Li & Bilong Liu & Jiang Liu & Zhen Liu & Dijia Luo, 2022. "Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    17. Alireza Ermagun & David Levinson, 2017. "Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending," Working Papers 000166, University of Minnesota: Nexus Research Group.
    18. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    19. 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).
    20. Zheng, Yan & Wang, Shengyou & Dong, Chunjiao & Li, Wenquan & Zheng, Wen & Yu, Jingcai, 2022. "Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

    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:eee:intfor:v:13:y:1997:i:1:p:63-72. 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/locate/ijforecast .

    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.