# Deep learning based black spot identification on Greek road networks

arXiv: https://arxiv.org/abs/2306.10734 · HTML: https://mlsanity.com/paper/2306.10734/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 47 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Comparative analysis of methods to estimate the tire/road friction coefficient applied to traffic accident reconstruction](https://mlsanity.com/paper/1908.07952/) (2019)

## Built on by (most cited first, 1 of 1)

- [Angular Regularization for Positive-Unlabeled Learning on the Hypersphere](https://mlsanity.com/paper/2512.06785/) (2025)

## Abstract (arXiv)

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.