IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v11y2019i12p247-d289850.html
   My bibliography  Save this article

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

Author

Listed:
  • Xin Zhou

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Peixin Dong

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Jianping Xing

    (School of Microelectronics, Shandong University, Jinan 250100, China)

  • Peijia Sun

    (School of Microelectronics, Shandong University, Jinan 250100, China)

Abstract

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

Suggested Citation

  • Xin Zhou & Peixin Dong & Jianping Xing & Peijia Sun, 2019. "Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network," Future Internet, MDPI, vol. 11(12), pages 1-11, November.
  • Handle: RePEc:gam:jftint:v:11:y:2019:i:12:p:247-:d:289850
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/11/12/247/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/11/12/247/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peixin Dong & Dongyuan Li & Jianping Xing & Haohui Duan & Yong Wu, 2019. "A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location," Future Internet, MDPI, vol. 11(4), pages 1-12, April.
    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. Marco Ferretti & Ugo Fiore & Francesca Perla & Marcello Risitano & Salvatore Scognamiglio, 2022. "Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development," Future Internet, MDPI, vol. 14(8), pages 1-19, July.

    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.

      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:gam:jftint:v:11:y:2019:i:12:p:247-:d:289850. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.