IDEAS home Printed from https://ideas.repec.org/a/jof/jforec/v25y2006i3p173-192.html
   My bibliography  Save this article

Comparison of two non-parametric models for daily traffic forecasting in Hong Kong

Author

Listed:
  • Y. F. Tang

    (Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Hong Kong)

  • William H. K. Lam

    (Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Hong Kong)

  • Mei-Lam Tam

    (Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Hong Kong)

Abstract

The most up-to-date annual average daily traffic (AADT) is always required for transport model development and calibration. However, the current-year AADT data are not always available. The short-term traffic flow forecasting models can be used to predict the traffic flows for the current year. In this paper, two non-parametric models, non-parametric regression (NPR) and Gaussian maximum likelihood (GML), are chosen for short-term traffic forecasting based on historical data collected for the annual traffic census (ATC) in Hong Kong. These models are adapted as they are more flexible and efficient in forecasting the daily vehicular flows in the Hong Kong ATC core stations (in total of 87 stations). The daily vehicular flows predicted by these models are then used to calculate the AADT of the current year, 1999. The overall prediction and comparison results show that the NPR model produces better forecasts than the GML model using the ATC data in Hong Kong. Copyright © 2006 John Wiley _ Sons, Ltd.

Suggested Citation

  • Y. F. Tang & William H. K. Lam & Mei-Lam Tam, 2006. "Comparison of two non-parametric models for daily traffic forecasting in Hong Kong," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 173-192.
    • Handle: RePEc:jof:jforec:v:25:y:2006:i:3:p:173-192
    DOI: 10.1002/for.984
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/for.984
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.984?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. Mulhern, Francis J. & Caprara, Robert J., 1994. "A nearest neighbor model for forecasting market response," International Journal of Forecasting, Elsevier, vol. 10(2), pages 191-207, September.
    2. Tebaldi, Claudia & West, Mike & Karr, Alan F, 2002. "Statistical Analyses of Freeway Traffic Flows," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(1), pages 39-68, January.
    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. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    2. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. R. K. Jha & B. S. Sahay & Manojit Chattopadhyay & Yuvraj Gajpal, 2018. "A visual approach to enhance coordination among diagnostic units using self-organizing map," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(1), pages 27-41, March.
    4. Amos Golan & Jeffrey M. Perloff, 2004. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 433-438, February.
    5. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.
    6. Bozos, Konstantinos & Nikolopoulos, Konstantinos, 2011. "Forecasting the value effect of seasoned equity offering announcements," European Journal of Operational Research, Elsevier, vol. 214(2), pages 418-427, October.
    7. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    8. Mike West, 2020. "Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 1-31, February.
    9. Catriona M. Queen & Ben J. Wright & Casper J. Albers, 2008. "Forecast covariances in the linear multiregression dynamic model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 175-191.
    10. Agnon, Yehuda & Golan, Amos & Shearer, Matthew, 1999. "Nonparametric, nonlinear, short-term forecasting: theory and evidence for nonlinearities in the commodity markets," Economics Letters, Elsevier, vol. 65(3), pages 293-299, December.
    11. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).
    12. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.

    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:jof:jforec:v:25:y:2006:i:3:p:173-192. 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: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.