Deep learning based black spot identification on Greek road networks
This work addresses road safety by identifying accident-prone areas in Greece, but it appears incremental as it applies deep learning to a known problem with new data.
The study tackled black spot identification on Greek road networks by analyzing traffic accident data from police and government reports, resulting in the creation of a publicly available dataset called Black Spots of North Greece (BSNG) and a highly accurate identification method.
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.