 Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regressionBrent Selby and Kara M. Kockelman Journal of Transport Geography, 2013, vol. 29, issue C, 24-32 Abstract: This work explores the application of two distinctive spatial methods for prediction of average daily traffic counts across the Texas network. Results based on Euclidean distances are compared to those using network distances, and both allow for strategic spatial interpolation of count values while controlling for each roadway’s functional classification, lane count, speed limit, and other site attributes. Both universal kriging and geographically weighted regression (GWR) are found to reduce errors (in practically and statistically significant ways) over non-spatial regression techniques, though errors remain quite high at some sites, particularly those with low counts and/or in less measurement-dense areas. Nearly all tests indicated that the predictive capabilities of kriging exceed those of GWR by average absolute errors of 3–8%. Interestingly, the estimation of kriging parameters by network distances show no enhanced performance over Euclidean distances, which require less data and are much more easily computed. Keywords: Annual average daily traffic (AADT) prediction; Universal kriging; Traffic counts; Geographically weighted regression (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:29:y:2013:i:c:p:24-32 DOI: 10.1016/j.jtrangeo.2012.12.009 Access Statistics for this article Journal of Transport Geography is currently edited by Frank Witlox More articles in Journal of Transport Geography from Elsevier |
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