Comprehensive Analysis of Traffic Accidents in Seoul: Major Factors and Types Affecting Injury Severity

Hyeonchoel Jeong,Keejun Han,Jungeun Kim,Inhi Kim

doi:10.3390/app12041790

Abstract

Accident and fatality rates of traffic accidents worldwide are steadily increasing every year; thus, considerable effort has been made to prevent traffic accidents and prepare countermeasures. This study aims to identify the major factors and types that affect the severity of traffic accidents in Seoul by utilizing the Seoul Metropolitan Government’s traffic accident dataset. To achieve this, we perform a comprehensive analysis by adopting various machine learning techniques—not only supervised learning methods but also unsupervised learning methods. As a result of the experiment, we derived several critical factors that were found to affect the severity of traffic accidents via supervised learning methods (i.e., ensemble-based and regression-based algorithms) and discovered dominant accident types via unsupervised learning methods (i.e., clustering-based algorithms). One of our primary findings is that, in contrast to common sense, environmental factors such as weather, season, and day of the week do not significantly affect the severity of traffic accidents in Seoul. Moreover, all methods highlight the importance of pedestrian-related factors, implying that it is highly necessary to prepare more meticulous institutional measures for pedestrians to reduce the negative influence of serious traffic accidents in Seoul.

Highlights

Traffic accidents have emerged as a serious social problem today, as the number of car registrations has increased rapidly owing to global economic growth and improvements in living standards [1,2,3]
We used a traffic accident dataset, which included accidents in Seoul from 2010 to 2018, to identify the major factors and types that affect the severity of traffic accidents
To create a good classification, less frequent or skewed data were pre-processed by being removed and re-grouped, and analyzed using XGBoost, Logistic Regression, and DBSCAN, which are the representative methodologies widely used in the field

Summary

Introduction

Traffic accidents have emerged as a serious social problem today, as the number of car registrations has increased rapidly owing to global economic growth and improvements in living standards [1,2,3]. It is necessary to identify major factors and types of traffic accidents to prevent traffic accidents in advance based on the results obtained. Along these lines, a number of related studies and policies are being carried out abroad. Seoul is the largest city in South Korea, with various types of transportations used by almost 10 million citizens and vehicles every day, implying that the traffic accidents would cause tremendous social and economic losses

Methods

Results

Conclusion