IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v527y2019ics0378437119306508.html
   My bibliography  Save this article

An adaptive hybrid model for short-term urban traffic flow prediction

Author

Listed:
  • Hou, Qinzhong
  • Leng, Junqiang
  • Ma, Guosheng
  • Liu, Weiyi
  • Cheng, Yuxing

Abstract

With the rapid increase in car ownership, urban transport systems are challenged by the overwhelming traffic demand and congestion. Dynamic prediction of traffic flows is of considerable significance for congestion mitigation and demand management. Real-time and precise prediction models are capable of analyzing traffic flow characteristics, predicting traffic flow trends, and motivating reasonable inductive actions. Considering the periodicity and variability of traffic flow and limitations of single prediction models, an adaptive hybrid model for predicting short-term traffic flow was proposed in this study. Firstly, the linear Autoregressive Integrated Moving Average (ARIMA) method and non-linear Wavelet Neural Network (WNN) method were used to predict traffic flow. Then, outputs of the two individual models were analyzed and combined by fuzzy logic and the weighted result was regarded as the final predicted traffic volume of the hybrid model. The results indicate that the hybrid model can offer better performance in predicting short-term traffic flow than the two single models either in stable or in fluctuating conditions. The relative error is within ±10%, showing that the proposed hybrid model is both accurate and reliable.

Suggested Citation

  • Hou, Qinzhong & Leng, Junqiang & Ma, Guosheng & Liu, Weiyi & Cheng, Yuxing, 2019. "An adaptive hybrid model for short-term urban traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119306508
    DOI: 10.1016/j.physa.2019.121065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119306508
    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.2019.121065?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.

    Citations

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


    Cited by:

    1. 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.
    2. Ismail Shah & Izhar Muhammad & Sajid Ali & Saira Ahmed & Mohammed M. A. Almazah & A. Y. Al-Rezami, 2022. "Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    3. Peng, Yanni & Xiang, Wanli, 2020. "Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    4. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K. & Cao, Shuhan, 2023. "A generative model for vehicular travel time distribution prediction considering spatial and temporal correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    5. 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).

    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:527:y:2019:i:c:s0378437119306508. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.