Addressing the Local-Road VMT Estimation Problem Using Spatial Interpolation Techniques

Abstract

Vehicle miles of travel (VMT) data have a wide range of applications in highway agency business processes. However, at all administrative levels, highway agencies continue to be stymied by the poor reliability of local-road VMT estimates resulting from the inadequacy of local-road traffic counts. This paper presents a methodology to address this problem. The methodology first clusters local roads and then imputes traffic-volume data for segments within each cluster by applying spatial interpolation techniques and sparse traffic-volume data. The proposed methodology uses geographic information system (GIS)-enabled spatial interpolation algorithms, including Kriging, inverse distance weighting (IDW), natural neighbor (NN), and trend techniques. The accuracy, in terms of prediction error, of each technique was validated using actual traffic counts. Spatial interpolation techniques can yield efficient imputations of absent traffic data and therefore can produce reliable estimates of local-road VMT. The results in this paper suggest that the use of spatial interpolation for local-road VMT estimation is cost-effective because it makes use of the available traffic-count data from existing road segments and therefore does not require additional data collection efforts. Also, a comparison was made of the relative efficacies of the alternative spatial interpolation techniques for purposes of imputing missing traffic data at certain links and ultimately for VMT estimation or prediction. The methodology can be updated easily with new traffic-count data and can be used by any highway agency for local-road VMT estimation. An essential prerequisite is a comprehensive inventory of the local roads for which the VMT is sought.