Predicting Traffic Accident Risk in Seoul Metropolitan City: A Dataset Construction Approach

Abstract

In contemporary society, the rapid progression of urbanization and technological advancements has led to a substantial increase in the number of vehicles, consequently elevating the rate of traffic accidents. To minimize the human and economic losses resulting from these accidents, extensive research has been conducted over the past decades. Recent studies utilizing Grid Maps and time-series methods have shown promising results in identifying factors related to traffic accidents and predicting accident occurrence rates. However, most existing research employs data without thoroughly analyzing its definitive correlation with traffic accidents, focusing solely on an overarching integration process or exclusively considering road conditions, while neglecting environmental factors surrounding the roads. Therefore, this paper focuses on a detailed analysis of factors contributing to traffic accidents in Seoul, the capital city of South Korea, limiting the study period from January 2020 to December 2021. The data encompasses various aspects such as traffic accidents, weather conditions, standard node links, traffic speed, Points of Interest (POI), solar altitude and azimuth, speed bumps, and traffic surveillance cameras. Most of this data is available from national public institutions, with some being computed through specific formulas. Since weather and POI data possess a wide range of features, a Pearson correlation analysis is conducted to extract features relevant to traffic accidents. The extracted features are then compiled and normalized to facilitate the deep learning model’s training, thereby constructing a comprehensive dataset.

Keywords

• Traffic accidents
• dataset construction
• correlation analysis
• data preprocessing
• deep learning