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.

本文介绍了黑点识别这一空间时间现象，并说明了该分析可以通过特定地点和时间段内道路碰撞的高发集中区域（即称为黑点）来确定交通事故发生的根本原因。此研究关注希腊道路网络上的交通事故，旨在识别黑点，利用警方和政府发布的车祸报告数据，生产了一个公开可用的数据集，即北希腊的黑点（BSNG），以及高度准确的识别方法。