Factors Determining Seasonal Variations in Traffic Volumes

M. Spławińska 1
  • 1 Cracow University of Technology, Faculty of Civil Engineering, 31-155, Krakow, Poland

Abstract

The characteristics of seasonal variations in traffic volumes are used for a variety of purposes, for example to determine the basic parameters describing annual average daily traffic – AADT, and design hourly volume – DHV, analyses of road network reliability, and traffic management. Via these analyses proper classification of road sections into appropriate seasonal factor groups (SFGs) has a decisive influence on results. This article, on the basis of computational experiments (models of artificial neural networks, discriminatory analysis), aims to identify which factors have the greatest impact on the allocation of a section of road to the corresponding SFG, based on short-term measurements. These factors are presented as qualitative data: the Polish region, spatial relationships, functions of road, cross-sections, technical class; and quantitative data: rush hour traffic volume.

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

  • 2. K. W. Axhausen, B. Jäggi, Ch.: DoblerBemessungsverkehrsstärken: Einneuer Ansatz. Forschungsprojekt VSS 2011/103 auf Antrag des SchweizerischenVerbands der Strassen und Verkehrsfachleute (VSS). Zürich, Juli 2015

  • 3. P. H. Bellamy.: Seasonal Variations in Traffic Flows, Supplementary Report 437, prepared for the Department of the Environment and the Department of Transport, Prepared by Traffic Engineering Department, Transport and Road Research Laboratory, Berkshire, Great Britain, 1978

  • 4. A. Faghri, J. Hua: Roadway Seasonal Classification Using Neural Network. Journal of Computing in Civil Engineering, Vol. 9, No.3, 1995, s. 209 ÷215

  • 5. Federal Highway Administration (FHWA), Traffic Monitoring Guide, 2013

  • 6. M. Gastaldi, G. Gecchele, R. Rossi “Estimation of Annual Average Daily Traffic from one-week traffic counts a Combined ANN-Fuzzy approach” Transportation Research Part C 47 (2014) 86-99

  • 7. Highway Capacity Manual, Sixth Edition: A Guide for Multimodal Mobility Analysis. Washington, D.C.: Transportation Research Board 2017

  • 8. M. T. Li, F. Zhao, Y. Wu: Application of Regression Analysis for Estimating Seasonal Factors in Southeast Florida. Transport Research Record 1870, Washington DC 2004, s. 153÷ 161

  • 9. P. Lingras: Classifying Highways: Hierarchical Grouping versus Kohonen Neural Networks. Journal of Transportation Engineering, 07/08.1995, s. 364 ÷ 368.

  • 10. P. Lingras: Statistical and Genetic Algorithms Classification of Highways. Journal of Transportation Engineering, Vol. 127, No.3, 2001, s.237 ÷ 243

  • 11. T. Pamuła: Classification and Prediction of Traffic Flow Based on Real Data Using Neural Networks. Archives of Transport, No. 12/2012, pp.519-529

  • 12. L. Pinkofsky: Typisierung von Ganglinien der Verkehrsstärke Und ihreEignungzurModellierung der Verkehrsnachfrage. Dissertation, TechnischeUniversitatBraunschweig, Aachen 2006

  • 13. S. G. Ritche: A Statistical Approach to Statewide Traffic Counting. Transportation Research Record 1090, Washington, DC, 1986, s. 14 ÷ 21

  • 14. Ruch Drogowy 2015, GDDKiA, Warszawa 2016

  • 15. S. C. Sharma: Improved Classification of Canadian Primary Highways According to Type of Road Use. Canadian Journal of Civil Engineering, Vol. 10, No.3, 1983, s. 497 ÷ 509

  • 16. S. C. Sharma, P. J. Lingras, M. U. Hassan, N. A. Murthy: Road Classification According to Driver Population. Transportation Research Record 1090, Washington, DC, 1986, s. 61÷ 69

  • 17. SHRP 2 – L08: Incorporation of Travel Time Reliability into the HCM, August 2013

  • 18. M. Spławiska: Models for determining annual average daily traffic on the national roads, Archives of Civil Engineering, nr 2/2015, s. 141 - 158

  • 19. M. Spławiska: Characteristics of traffic flow variability and their impact on AADT. LAP Lambert Academic Publishing, 2015

  • 20. A. Stanisz: Przystpny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny, tom III. Kraków 2007

  • 21. I. Tsapakis, W. Schneider, A. Bolbol, A. Skarlatidou: Discriminant Analysis for Assigning Short-Term Counts to Seasonal Adjustment Factor Groupings. Transportation Research Record: Journal of the Transportation Research Board 2256, Washington DC 2011, s. 112 ÷ 119

  • 22. Verkehrsentwicklung auf Bundesfernstaben 2007. Berichte der Bundesanstalt fur Strabenwesen, Verkehrstechnik Heft V 178

  • 23. M. Li, F. Zhao, L. Chow. Assignment of Seasonal Factor Categories to Urban Coverage Count Stations Using a Fuzzy Decision Tree. ASCE Journal of Transportation Engineering, Vol. 132, No. 8, 2006, pp. 654-662

  • 24. http://www.gddkia.gov.pl/userfiles/articles/s/stacje-ciaglych-pomiarow-ruchu-d_26174/R02_05_2016.pdf

  • 25. https://www.gddkia.gov.pl/userfiles/articles/p/pismo-przewodnie-z-dnia-15032007_4423/Zalacznik_3_Prognozy_wzrostu_PKB_2008-40_poprawa_kodow_nts.pdf

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