Models For Determining Annual Average Daily Traffic On The National Roads

• Main Author: Spławińska M.
• Format: Article
• Language: English
• Published: Sciendo 2015-06-01
• Series: Archives of Civil Engineering
• Subjects: roads; traffic flow variability; Annual Average Daily Traffic (AADT); multiple regression; artificial neural networks; drogi; zmienność natężeń ruch; Średni Dobowy Ruch w roku (SDR); regresja wieloraka; sztuczne sieci neuronowe
• Online Access: http://www.degruyter.com/view/j/ace.2015.61.issue-2/ace-2015-0019/ace-2015-0019.xml?format=INT

One of the basic parameters which describes road traffic is Annual Average Daily Traffic (AADT). Its accurate determination is possible only on the basis of data from the continuous measurement of traffic. However, such data for most road sections is unavailable, so AADT must be determined on the basis of short periods of random measurements. This article presents different methods of estimating AADT on the basis of daily traffic (VOL), and includes the traditional Factor Approach, developed Regression Models and Artificial Neural Network models. As explanatory variables, quantitative variables (VOL and the share of heavy vehicles) as well as qualitative variables (day of the week, month, level of AADT, the cross-section, road class, nature of the area, spatial linking, region of Poland and the nature of traffic patterns) were used. Based on comparisons of the presented methods, the Factor Approach was identified as the most useful.