Deep learning based black spot identification on Greek road networks
Ioannis Karamanlis Alexandros Kokkalis Vassilios Profillidis George Botzoris Chairi Kiourt Vasileios Sevetlidis George Pavlidis
Black spot identification, a spatiotemporal phenomenon, involves analyzing
the geographical location and time-based occurrence of road accidents.
Typically, this analysis examines specific locations on road networks during
set time periods to pinpoint areas with a higher concentration of accidents,
known as black spots. By evaluating these problem areas, researchers can
uncover the underlying causes and reasons for increased collision rates, such
as road design, traffic volume, driver behavior, weather, and infrastructure.
However, challenges in identifying black spots include limited data
availability, data quality, and assessing contributing factors. Additionally,
evolving road design, infrastructure, and vehicle safety technology can affect
black spot analysis and determination. This study focused on traffic accidents
in Greek road networks to recognize black spots, utilizing data from police and
government-issued car crash reports. The study produced a publicly available
dataset called Black Spots of North Greece (BSNG) and a highly accurate
identification method.