A GEOGRAPHIC WEIGHTED REGRESSION FOR RURAL HIGHWAYS CRASHES MODELLING USING THE GAUSSIAN AND TRICUBE KERNELS: A CASE STUDY OF USA RURAL HIGHWAYS
- 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
- 2School of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
Keywords: Geographic Weighted Regression, Ordinary Least Squares, Rural highways crashes modelling, USA Highways, Spatial autocorrelation, Spatial non-stationarity
Abstract. Based on world health organization (WHO) report, driving incidents are counted as one of the eight initial reasons for death in the world. The purpose of this paper is to develop a method for regression on effective parameters of highway crashes. In the traditional methods, it was assumed that the data are completely independent and environment is homogenous while the crashes are spatial events which are occurring in geographic space and crashes have spatial data. Spatial data have spatial features such as spatial autocorrelation and spatial non-stationarity in a way working with them is going to be a bit difficult. The proposed method has implemented on a set of records of fatal crashes that have been occurred in highways connecting eight east states of US. This data have been recorded between the years 2007 and 2009. In this study, we have used GWR method with two Gaussian and Tricube kernels. The Number of casualties has been considered as dependent variable and number of persons in crash, road alignment, number of lanes, pavement type, surface condition, road fence, light condition, vehicle type, weather, drunk driver, speed limitation, harmful event, road profile, and junction type have been considered as explanatory variables according to previous studies in using GWR method. We have compered the results of implementation with OLS method. Results showed that R2 for OLS method is 0.0654 and for the proposed method is 0.9196 that implies the proposed GWR is better method for regression in rural highway crashes.