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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17000

Title: Application of Geographically Weighted Regression Technique in Spatial Analysis of Fatal and Injury Crashes
Authors: PIRDAVANI, Ali
WETS, Geert
Issue Date: 2014
Abstract: Generalized linear models (GLMs) are the most widely used models utilized in crash prediction studies. These models illustrate the relationships between the dependent and explanatory variables by estimating fixed global estimates. Since crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is examined by means of calculating Moran’s I measures for dependent and explanatory variables. The results indicate the necessity of considering spatial correlation when developing crash prediction models. The main objective of this research is to develop different zonal crash prediction models (ZCPMs) within the geographically weighted generalized linear model (GWGLM) framework in order to explore the spatial variations in association between number of injury crashes (NOICs) (including fatal, severely, and slightly injured crashes) and other explanatory variables. Different exposure, network, and sociodemographic variables of 2,200 traffic analysis zones (TAZs) are considered as predictors of crashes in the study area, Flanders, Belgium. To this end, an activity-based transportation model framework is applied to produce exposure measurements while the network and sociodemographic variables are collected from other sources. Crash data used in this study consist of recorded crashes between 2004 and 2007. The performances of developed GWGLMs are compared with their corresponding GLMs. The results show that GWGLMs outperform GLMs; this is due to the capability of GWGLMs in capturing the spatial heterogeneity of crashes.
Notes: Pirdavani, A (reprint author), Res Fdn Flanders FWO, Egmontstr 5, B-1000 Brussels, Belgium. ali.pirdavani@uhasselt.be; tom.bellemans@uhasselt.be; tom.brijs@uhasselt.be; geert.wets@uhasselt.be
URI: http://hdl.handle.net/1942/17000
DOI: 10.1061/(ASCE)TE.1943-5436.0000680
ISI #: 000340176400003
ISSN: 0733-947X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

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