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Article

Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

by 1, 2 and 1,*
1
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
2
Division of Research and Development, Indiana Department of Transportation, West Lafayette, IN 47906, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Mei-Po Kwan and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(5), 286; https://doi.org/10.3390/ijgi10050286
Received: 13 March 2021 / Revised: 22 April 2021 / Accepted: 26 April 2021 / Published: 30 April 2021
Vehicle crashes on roads are caused by many factors. However, the influence of these factors is not necessarily homogenous across locations, which is a challenge for non-stationary modeling approaches. To address this problem, this paper adopts two types of methods allowing parameters to fluctuate among observations, that is, the random parameter approach and the geographically weighted regression (GWR) approach. With road curvature, curve length, pavement friction, and traffic volume as independent variables, vehicle crash frequencies are modeled by two non-spatial methods, including the negative binomial (NB) model and random parameter negative binomial (RPNB), as well as three spatial methods (GWR approach). These models are calibrated in microlevel using a dataset of 9415 horizontal curve segments with a total length of 1545 kilometers for a period of three years (2016–2018) over the State of Indiana. The results revealed that the GWR approach can capture spatial heterogeneity and therefore significantly outperforms the conventional non-spatial approach. Based on the Akaike Information Criterion (AICc), geographically weighted negative binomial regression (GWNBR) was proved to be a superior approach for statewide microlevel crash analysis. View Full-Text
Keywords: spatial heterogeneity; random parameter negative binomial model; geographically weighted regression; transportation; vehicle crash spatial heterogeneity; random parameter negative binomial model; geographically weighted regression; transportation; vehicle crash
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MDPI and ACS Style

Wang, C.; Li, S.; Shan, J. Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression. ISPRS Int. J. Geo-Inf. 2021, 10, 286. https://doi.org/10.3390/ijgi10050286

AMA Style

Wang C, Li S, Shan J. Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression. ISPRS International Journal of Geo-Information. 2021; 10(5):286. https://doi.org/10.3390/ijgi10050286

Chicago/Turabian Style

Wang, Ce, Shuo Li, and Jie Shan. 2021. "Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression" ISPRS International Journal of Geo-Information 10, no. 5: 286. https://doi.org/10.3390/ijgi10050286

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