| description abstract | Transportation planning requires the use of accurate traffic data to produce estimates of traffic volume predictions over time and space. The annual average daily traffic (AADT) data is an important component of transportation design, operation, policy analysis, and planning. The use of traffic volume forecasting models for the characterization, analysis, and estimation of transportation data has proven to be a useful method for reducing high costs, overcoming spatial constraints, and limiting the errors associated with data collection and analysis in transportation planning. The geostatistical kriging technique is a viable method for modeling and forecasting AADT. The degree to which the technique of kriging can be useful in forecasting AADT depends highly on an understanding of the decision-making variables, the relationship between the variables, and the practical limitations of the various kriging techniques and variogram models. This paper applied three different linear kriging techniques [simple kriging (SK), ordinary kriging (OK), and universal kriging (UK)] and five variogram models (nugget effect, spherical, exponential, Gaussian, and power) to characterize and interpolate the annual average daily traffic of Washington State. The statistical errors (i.e., mean error, root-mean-square error, average standard error, mean standardized error, and root-mean-square standardized error) of the resulting output were then compared to determine the most suitable combination of linear spatial interpolation and variogram algorithms for each of the data sets. Ordinary and/or universal kriging combined with the exponential variogram were the most appropriate methods for the 2008 AADT data set, whereas ordinary kriging combined with the spherical variogram and simple kriging combined with the spherical variogram were the most suitable methods for the 2009 and 2010 data sets, respectively. Results from this study suggest that using the same combination of kriging and variogram algorithms to characterize and interpolate different AADT data sets (2008, 2009, and 2010) could lead to suboptimal results. One reason for the lack of optimal results is for instance, the directional variation in the 2009 and 2010 data sets which undermine the assumption of mean stationarity in the use of ordinary kriging for modeling. | |
