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Analysis of Global Road Traffic Death Data Using a Clustering Approach
Department of Civil, Architectural & Environmental Engineering, Detroit, USA
Department of Mathematics, University of Detroit Mercy, Detroit, USA
Department of Civil, Architectural & Environmental Engineering, Detroit, USA
- 1 Department of Civil, Architectural & Environmental Engineering, Detroit, USA
- 2 Department of Mathematics, University of Detroit Mercy, Detroit, USA
- 3 Department of Civil, Architectural & Environmental Engineering, Detroit, USA
Current Urban Studies·Volume 10 (2022)·Pages 275–292·Published 12 May 2022·DOI10.4236/cus.2022.102017
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Abstract
Three years of global traffic death data were analyzed to identify significant socio-economic determinants of global traffic death. Due to wide variation of data elements, the collected data were standardized and then four clusters namely Medium, Low, High and Medium-High were formed. The stepwise regression analysis method was used to find significant determinants. Proportion of urban population, alcohol, number of registered vehicles per million of population and human development index were appeared as significant determinants. However, income was noticed as a significant variable in the case of only cluster Low. The involvement of older drivers in traffic death incident was very low.
KeywordsGlobal Traffic DeathClustersDeterminantsRegressionSocio-Economic Factors
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