Black spot identification, a spatiotemporal phenomenon, involves analysing 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 behaviour, 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.

The utilization of conclusions from the data analysis of road traffic accidents is of high importance for the development of targeted traffic safety measures, which will effectively reduce the rate of road traffic accidents, thus promoting road safety. Considering the problems of time and money, it is not practical to improve road safety in all the places where road traffic accidents occur. Therefore, the process of identifying accident-prone locations, known as black spots, is a cost-effective and efficient way to analyze the causes of road accidents and reduce them. Identifying black spots is an effective strategy to reduce accidents. The core methods that may be used in the process of identifying the black spots of a road network are the sorting, grouping, and accident prediction methods. However, in practice, it is easy to overlook certain factors that significantly contribute to defining and characterizing a spot on the road network as black. Therefore, suggestions to carry out projects required to reduce security risks shall not be based on the above methods. Machine learning algorithms that in recent years have been widely used in the field of predicting a road traffic accident cover these weaknesses. They can effectively classify data sets and make a connection between factors and the severity of events. Machine learning algorithms include classification, regression, clustering, and dimensionality reduction. In this work, a study was conducted on road traffic accidents that took place on the national and provincial network of Northern Greece from 2014 to 2018, with the aim of determining the black spots. The study provided the general public access to a database of black spots on the road network of Northern Greece. At the same time, it created a point of reference for the recognition of the points in question located on the entire road network, and selected a black spot determination model, after having compared specific measures to determine the quality of a model, which resulted from the application of a logistic regression and machine learning algorithms.

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