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Advances in Regression Kriging-Based Methods for Estimating Statewide Winter Weather Collisions: An Empirical Investigation

Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2W2, Canada
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Academic Editor: Luigi dell’Olio
Future Transp. 2021, 1(3), 570-589; https://doi.org/10.3390/futuretransp1030030
Received: 3 August 2021 / Revised: 3 September 2021 / Accepted: 24 September 2021 / Published: 13 October 2021
Winter conditions create hazardous roads that municipalities work hard to maintain to ensure the safety of the travelling public. Targeting their efforts with effective network screening will help transportation managers address these problems. In our recent efforts, regression kriging was found to be a viable and effective network screening methodology. However, the study was constrained by its limited spatial extent making the reported results less conclusive and transferrable. In addition, our previous work implemented what has long been adopted in most of conventional studies—the Euclidean distance; however, use of the road network distance would, intuitively, result in further improving kriging estimates, especially when dealing with transportation problems. Therefore, this study improves upon our previous efforts by developing a more advanced kriging model; namely, network regression kriging using the entire state of Iowa with the significantly expanded road network. The transferability of the developed models is also explored to investigate its generalization potential. The findings based on various statistical measures suggest that the enhanced kriging model vastly improved the estimation performance at the cost of greater computational complexity and run times. The study also suggests that regional semivariograms better represent the true nature of the local variances, though an overall model may still function adequately if higher fidelity is not required. View Full-Text
Keywords: regression kriging (RK); road network distances; network screening; geostatistics; second order stationarity assumption regression kriging (RK); road network distances; network screening; geostatistics; second order stationarity assumption
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MDPI and ACS Style

Wong, A.H.; Kwon, T.J. Advances in Regression Kriging-Based Methods for Estimating Statewide Winter Weather Collisions: An Empirical Investigation. Future Transp. 2021, 1, 570-589. https://doi.org/10.3390/futuretransp1030030

AMA Style

Wong AH, Kwon TJ. Advances in Regression Kriging-Based Methods for Estimating Statewide Winter Weather Collisions: An Empirical Investigation. Future Transportation. 2021; 1(3):570-589. https://doi.org/10.3390/futuretransp1030030

Chicago/Turabian Style

Wong, Andy H., and Tae J. Kwon 2021. "Advances in Regression Kriging-Based Methods for Estimating Statewide Winter Weather Collisions: An Empirical Investigation" Future Transportation 1, no. 3: 570-589. https://doi.org/10.3390/futuretransp1030030

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