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Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regression

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  • Selby, Brent
  • Kockelman, Kara M.

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.

Suggested Citation

  • Selby, Brent & Kockelman, Kara M., 2013. "Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 29(C), pages 24-32.
    • Handle: RePEc:eee:jotrge:v:29:y:2013:i:c:p:24-32
    DOI: 10.1016/j.jtrangeo.2012.12.009
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    1. Morency, Catherine & Paez, Antonio & Roorda, Matthew J. & Mercado, Ruben & Farber, Steven, 2011. "Distance traveled in three Canadian cities: Spatial analysis from the perspective of vulnerable population segments," Journal of Transport Geography, Elsevier, vol. 19(1), pages 39-50.
    2. Haonan Wang & M. Giovanna Ranalli, 2007. "Low-Rank Smoothing Splines on Complicated Domains," Biometrics, The International Biometric Society, vol. 63(1), pages 209-217, March.
    3. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    4. Collins, Sean, 1991. "Prediction Techniques for Box-Cox Regression Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 267-277, July.
    5. Sean Collins, 1991. "Prediction techniques for Box-Cox regression models," Finance and Economics Discussion Series 148, Board of Governors of the Federal Reserve System (U.S.).
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