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Archives of Civil Engineering

The Journal of Polish Academy of Sciences

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The Problem Of Imputation Of The Missing Data From The Continuous Counts Of Road Traffic

M. Spławińska
Published Online: 2015-09-30 | DOI: https://doi.org/10.1515/ace-2015-0009


Missing traffic data is an important issue for road administration. Although numerous ways can be found to impute them in foreign literature (inter alia, the most effective method, that is Box-Jenkins models), in Poland, still only proven and simplified methods are applied. The article presents the analyses including an assessment of the completeness of the existing traffic data and works related to the construction of SARIMA model. The study was conducted on the basis of hourly traffic volumes, derived from the continuous traffic counts stations located in the national road network in Poland (Golden River stations) from the years 2005 – 2010. As a result, the proposed model was used to impute the missing data in the form of SARIMA (1.1,1)(0,1,1)168. The newly developed model can be used effectively to fill in the missing required days of measurement for estimating AADT by AASHTO method. In other cases, due to its accuracy and laboriousness of the process, it is not recommended.

Keywords: roads; traffic data collection; imputation of the missing traffic data; model SARIMA


  • 1. Zbieranie, archiwizacja i analizy danych ze stacji ciągłych pomiarów ruchu, Etap III, Archiwizacja wyników pomiarów ze stacji GR i PAT oraz analiza wyników pomiarów ze stacji F-P, RPP-2 i RPP-5 za okres od 01.11 do 31.12.2004, Transprojekt – Warszawa Sp. z o.o., Warszawa 2005Google Scholar

  • 2. Zbieranie, archiwizacja i analizy danych ze stacji ciągłych pomiarów ruchu w roku 2008, Etap III, Analiza roczna i edycja wyników pomiarów prowadzonych w stacjach GR i PAT w roku 2009, Transprojekt – Warszawa Sp. z o.o., Warszawa 2010Google Scholar

  • 3. S. Sharma, P. Lingras, M. Zhong “Effect of Missing Values Estimations on Traffic Parameters”, Transporatation Planning and Technology, Vol.27, No.2, 119-144, 04.2004Google Scholar

  • 4. M. Zhong, P. Lingras, S. Sharma “Estimation of missing traffic counts using factor, genetic, neural, and regression techniques”, Transport Research Part C: Emerging Technologies Volume 12, Issue 2, 139-166, 04.2004Google Scholar

  • 5. AASHTO Guidelines for Traffic Data Programs, American Association of State Highway and Transportation Officials, 1992Google Scholar

  • 6. Federal Highway Administration (FHWA), Traffic Monitoring Guide, 2001Google Scholar

  • 7. S. Datla, S. Sharma “Consideration of Weather Conditions to Estimate Missing Traffic Data”, Transportation Research Record: Journal of the Transportation Research Board 2049, 71-80, Washington DC 2008Google Scholar

  • 8. M. Zhong, S. Sharma, P. Lingras “Genetically Designed Models for Accurate Imputations of Missing Traffic Counts”, Transport Research Record 1879, 71-79, Washington DC 2004Google Scholar

  • 9. M. Zhong, S. Sharma, P. Lingras “Matching Patterns for Updating Missing Values of Traffic Counts. Transportation Planning and Technology”, Vol. 29, No.2, 141 ÷ 156, 04.2006Google Scholar

  • 10. B. Ghosh, B. Basu, M. O’Mahony “A Bayesian Time-Series model for Short-Term Traffic Flow Forecasting”, Journal of Transportation Engineering, Volume 133, Issue 3, 180-189, 03.2007Google Scholar

  • 11. M. Sabry, H. Abd-El-Latif, S. Yousef, N. Badra “Use of Box and Jenkins Time Series Technique in Traffic Volume Forecasting”, Research Journal of Social Sciences, 83-90, 2007Google Scholar

  • 12. Y. Zhang, L. Yuncai “Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting”, World Academy of Science, Engineering and Technology 51, 236-242, 2009Google Scholar

  • 13. M. Zhong, S. Sharma, P. Lingras “Genetically-Designed Time Delay Neural Networks for Multiple-interval Urban Freeway Traffic Flow Forecasting”, Neural Information Processing – Letters and Reviews, Vol.10, Nos. 8-9, 201-209, 08.-09.2006Google Scholar

  • 14. P. Lingras, S. Sharma, M. Zhong “Prediction of Recreational Travel using Genetically Designed Regression and Time Delay Neural Network Models”, Transport Research Record 1805, 16-24, Washington DC 2002Google Scholar

  • 15. M. Zhong, S. Sharma, P. Lingras “Short-Term Traffic Prediction on Different Types of Roads with Genetically Designed Regression and Time Deley Neural Network Models”, Journal of Computing in Civil Engineering, 94-103, 01.2005Google Scholar

  • 16. M. Zhong, S. Sharma, P. Lingras “Refining Genetically designed Models for Improved Traffic Prediction on Rural Roads”, Transporatation Planning and Technology, Vol.28, No.3, 213-236, 06.2005Google Scholar

  • 17. A. Sokołowski, Prognozowanie i analiza szeregów czasowych. Kraków, 25 –26.06.2009Google Scholar

  • 18. A. Zeliaś, B. Pawełek, S. Wanat “Prognozowanie ekonomiczne teoria, przykłady, zadania”, PWN, Warszawa 2003Google Scholar

  • 19. A. Lichota “Prognozowanie krótkoterminowe na lokalnym rynku energii elektrycznej”, Rozprawa doktorska, Akademia Górniczo – Hutnicza, Kraków 2006Google Scholar

  • 20. M. Spławińska „Analiza stabilności charakterystyk zmienności natężeń ruchu w dłuższym okresie”, 9902-9911, Logistyka 6/2014Google Scholar

  • 21. T. Wright, P. S. Hu, J. Young, A. Lu “Variability in Traffic Monitoring Data”, Final Summary Report, Oak Ridge National Liboratory, US Department of Energy, 08.1997Google Scholar

  • 22. A. Olma „Określenie współczynników przeliczeniowych do szacowania natężeń ruchu drogowego w obszarach miejskich”, Praca doktorska, Politechnika Śląska, Gliwice 2005Google Scholar

About the article

Received: 2015-03-15

Revised: 2015-04-04

Published Online: 2015-09-30

Published in Print: 2015-03-01

Citation Information: Archives of Civil Engineering, Volume 61, Issue 1, Pages 131–145, ISSN (Online) 1230-2945, DOI: https://doi.org/10.1515/ace-2015-0009.

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© Polish Academy of Sciences. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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