Citation

Wachnicka J, Palikowska K, Kustra W, Kiec M. Accid. Anal. Prev. 2020; 150: e105849.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.aap.2020.105849

PMID

33310429

Abstract

Road safety varies significantly across the regions in Europe. To understand the factors behind this differentiation and the effects they have, data covering 263 NUTS-2 (Nomenclature of Territorial Units for Statistics) regions across Europe (European Union and Norway) have been analysed. The assessment was made using Geographically Weighted Regression (GWR). As a dependent variable the Road Fatality Rate (RFR - number of fatalities in a given year per one million population of the region) was used. The GWR was developed from 2014 data and took account of variables that characterise economic, infrastructural and social development. The model was validated using 2016-2018 data. The following factors were found to be statistically significant: gross domestic product per person (GDPPC), number of passenger cars per inhabitant (MRPC), share of passenger vehicles (PPC), life expectancy at birth (LIFE), as well as variables related to the border of the regions, innerborder (IB) and outerborder (OB).

RESULTS suggest that the GWR has an advantage over the global linear model which does not address regional proximity. The model allows for identification of the differences in the level of road safety in regions, estimated on the basis of the RFR and the available data in Eurostat databases. This in turn allows for indicating regions in which activities to improve road safety should have the highest priority. The model shows a large spatial diversity of factors affecting the RFR, which indicates the need to take different actions to improve road safety depending on the region. The results suggest that the GWR model can be useful for predicting and more efficient management of road safety at the regional level in Europe.

Language: en

Keywords

Road safety; Geographically Weighted Regression; GWR; NUTS-2; Road Fatality Rate; Spatial