Stress-Inducing Scenarios for Elderly Drivers: A Text Mining Approach Utilizing FGI-Based Analysis

Nakhyeon Choi; Junghwa Kim

doi:10.7470/jkst.2023.41.5.525

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2023 Vol.41, Issue 5 Preview Page Next Page

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Stress-Inducing Scenarios for Elderly Drivers: A Text Mining Approach Utilizing FGI-Based Analysis 심층면접과 텍스트 마이닝 기반의 고령 운전자 스트레스 유발 교통상황 시나리오 구축

31 October 2023. pp. 525-537

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Abstract

Recently, the number of accidents caused by drivers aged 65 and above on highways has been steadily increasing. The physical changes associated with aging among elderly drivers are known to be a contributing factor to these accidents. However, there is a lack of research that specifically identifies the circumstances under which accidents involving elderly drivers occur. In this study, we conducted a Focus Group Interview (FGI) with drivers aged 65 years and above who have brain and cardiovascular diseases to collect unstructured data on situations that induce stress. Through text mining, we derived five types of stressful situations for elderly drivers by categorizing the main topics as the causes of stress during driving and the sub topics as the traffic situations when they get stressed. We intended to construct scenarios for stressful situations for elderly drivers by combining the five types of main topic and sub topic. However, due to the lack of necessary sub topic for scenario construction, we extracted only accidents involving elderly drivers from traffic accident analysis system in Korea to build a database specifically focused on accidents caused by elderly drivers. By applying Latent Dirichlet Allocation (LDA) and identifying accident groups with topics matching the main topics of stressful situations for elderly drivers, we were able to supplement the sub topics and develop the scenarios for the elderly driver accident database. Our study can be used as a test scenario for traffic safety policy development in a super-aged society.

Keywords

elderly driver

focus group interview

latent dirichlet allocation

scenario development

text mining

최근 고속도로에서 65세 이상 운전자에 의한 사고 건수는 매년 점차 증가하고 있다. 고령 운전자의 노화로 인한 신체적 변화는 사고의 종합적인 원인으로 알려져 있다. 그러나 고령 운전자 사고가 발생하는 구체적인 상황을 규명하는 연구는 부족한 실정이다. 본 연구에서는 뇌·심혈관계 질환을 보유하고 있는 65세 이상 고령 운전자를 대상으로 Focus Group Interview(FGI)를 통해 실시하여 스트레스를 유발하는 상황에 대한 비정형 데이터를 수집하였다. FGI에서 수집한 데이터를 텍스트 마이닝 분석 결과, 주행 시 스트레스를 느끼는 원인의 토픽을 주요 키워드, 스트레스를 받을 때의 교통상황을 상황 키워드로 분류하여 총 5가지의 고령 운전자 스트레스 유발상황 유형을 도출하였다. 고령 운전자 스트레스 유발상황 시나리오는 5가지 유형의 주요 키워드와 상황 키워드를 결합하여 구축했지만, 시나리오 구축에 필요한 상황 키워드의 부재로 한국의 고속도로 교통사고 데이터를 수집하는 교통사고분석 시스템(TAAS)의 고속도로 사고 데이터에서 고령 운전자 사고만 추출하여 고령 운전자 교통사고 지식베이스를 구축하였다. 추가 구축한 지식베이스는 사고 건수가 많은 이유로 시나리오 유발상황 유형 주요 키워드와 적절한 상황 키워드 보완을 위해 Latent Dirichlet Allocation(LDA)를 실시하여 3개의 교통사고 그룹을 생성하고 고령 운전자 스트레스 유발상황 유형의 주요 키워드와 일치하는 토픽이 있는 사고그룹을 찾아 상황 키워드를 보완함으로써 최종적인 시나리오를 개발하였다.

키워드

고령 운전자

표적집단면적법

잠재 디리클레 할당

시나리오 개발

텍스트 마이닝

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Information

Publisher :Korean Society of Transportation
Publisher(Ko) :대한교통학회
Journal Title :Journal of Korean Society of Transportation
Journal Title(Ko) :대한교통학회지
Volume : 41
No :5
Pages :525-537
Received Date : 2023-05-24
Revised Date : 2023-06-09
Accepted Date : 2023-07-24
DOI :https://doi.org/10.7470/jkst.2023.41.5.525

Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

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