Data mining of driver characteristics to spatial and temporal hotspots of single vehicle crashes in Western Australia

TL;DRAbstract

This paper presents innovative methods for identifying the characteristics of drivers involved in single vehicle crashes (SVCs) in Western Australia when viewed spatially and temporally. Spatial and temporal hotspots of SVCs can be defined as clusters of SVCs that have exceeded the expected value over a certain time period and at certain locations. The EM (Expectation-Maximisation) algorithm was adopted to identify the characteristics of driver involved in vehicle crashes. Drivers were divided into different segments based on their socio-demographic characteristics, i.e., age and gender and driver related crash factors such as, drive license's types, alcohol consumption, speeding, fatigue and inattention etc. The spatial hotspots of the SVCs were identified using the Kernel Density Estimation method. Comap was used to integrate space, time and characteristics of drivers into one view to better understand the nature of the SVCs.

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