All Issue

2022 Vol.40, Issue 5 Preview Page

Article

Exploring the Features of Aggressive Driving and Road Rage by Analyzing Judicial Precedents Based on Topic Modeling

토픽모델링 기반의 판례분석을 통한 난폭운전과 보복운전 특성 비교 연구

Changhun KIM1
,
Suejung LEE2
,
Junghwa KIM3*
김 창훈1
,
이 수정2
,
김 정화3*
1Student, Department of Urban & Transportation Engineering, College of Creative Engineering, Kyonggi University, Gyeonggi 16227, Korea
2Professor, Department of Forensic Psychology, Kyonggi University, Gyeonggi 16227, Korea
3Assistant Professor, Department of Urban & Transportation Engineering, College of Creative Engineering, Kyonggi University, Gyeonggi 16227, Korea
1경기대학교 스마트시티공학부 도시 ‧ 교통공학전공 학부과정
2경기대학교 범죄심리학과 교수
3경기대학교 스마트시티공학부 도시 ‧ 교통공학전공 조교수
*Corresponding Author
31 October 2022. pp. 683-699
PDF XML Citation
Abstract

Case of damage caused by aggressive driving and road rage have been reported frequently through various media. Diversifying and serious crimes cause a lot of damage, creating anxiety for other drivers on the road. Compared with violence and social seriousness of the crime, research activities have not been conducted due to the lack of academic interest. The purpose of this study is to present a reference point of legal punishment for road rage crimes and policies to protect victims. we tried to understand the crime characteristics by case analysis using topic Modeling which infers potential variables within a set of documents. The analysis of case data was conducted through the process of collecting case data, data preprocessing, determining the optimal number of topics, topic modeling, and comparative analysis of characteristics. 23 Aggressive driving, 31 Road rage, a total of 54 case datas were collected by setting Classification criteria for avoid data duplication and quality improvement. Then data preprocessing was performed through four steps of index selection, term removal, nounization, with common word conversion and surplus keyword removal in case data. As a results of the topic Modeling analysis, four topics and sub keywords were derived to understand the characteristics of aggressive driving and road rage. Classified topic and keywords for Comparative analysis were analyzed in four types (behavior pattern, damage type, crime fact and judgment, crime target and place), then visualization work has been conducted using Radar Chart to compare the characteristics of aggressive driving and road rage include distinguish the similarities and differences between the two crimes. The methodology applied in this study confirmed specific crime forms and characteristics can be derived through case study, and it is expected to be used to develop police guidelines, crime prevention systems, and infrastructure to crack down on aggressive driving.

Keywords
aggressive driving
case study
characteristic study
road rage
topic modeling

여러 매체를 통해 난폭운전과 보복운전으로 인한 피해사례가 빈번하게 보도되고 있다. 다양해지고 심각해지는 가해 수법으로 피해사례가 늘어나고 있으며, 도로 위 운전자들의 불안감이 조성되고 있다. 해당 범죄의 폭력성, 사회적 심각성과 비교했을 때 이에 대한 정책적, 학술적 관심은 현저히 낮으며, 관련된 실증연구 또한 미흡한 수준이다. 본 연구에서는 가해자 처벌 및 피해자 구호를 위한 법률적 근거를 마련하는 데 있어 기준점을 제시하고자 문서 집합 내에 잠재 변수를 추론하는 기법인 토픽모델링을 활용하여 판례분석을 진행함으로써, 범죄 특성 및 특성 간 연관성을 파악하고자 하였다. 판례 데이터의 분석은 판례 데이터 수집, 데이터 전처리, 최적 토픽 수 결정, 토픽모델링, 특성 간 비교분석에 이르는 과정을 통해 진행되었다. 데이터의 중복 방지 및 품질 향상을 위한 기준을 마련하여 난폭운전 23건, 보복운전 31건 총 54건의 판례 데이터를 수집하였으며, 이후 비정형의 판례 데이터의 분석을 위해 판례 내 인덱스 선별, 불용어 제거, 명사화 및 공통단어 변환, 잉여 키워드 제거의 4단계 절차를 통해 데이터 전처리 작업을 수행하였다. 토픽모델링 분석 결과를 통해 난폭운전과 보복운전의 특성을 파악할 수 있는 각각 4가지의 잠재 토픽과 하위 키워드를 도출하였다. 도출된 토픽과 토픽 내 키워드들에서 비교 분석이 가능한 특성을 추출하여 4가지 유형(행동양상, 피해유형, 범죄사실과 판결, 범행대상과 장소)에 대한 분석을 진행하였으며, Radar Chart를 이용한 시각화를 통해 난폭운전과 보복운전의 특성 비교 및 두 범죄의 유사점과 차이점을 구분하였다. 본 연구 수행 과정에서 적용된 방법론은 판례를 통해 특정 범죄의 형태와 특성을 도출할 수 있음을 확인하였으며, 향후 난폭 ‧ 보복운전 관련 법률 제정 및 법률적 판단 기준을 마련하는 기초자료 제작과 함께 이를 활용한 난폭 ‧ 보복운전 단속을 위한 경찰 가이드라인, 범죄 예방 시스템 및 인프라 개발 등에 활용할 수 있을 것으로 기대된다.

키워드
난폭운전
판례분석
특성 분석
보복운전
토픽모델링
References
1
AAA Foundation for Traffic Safety, Road Rage: How to Avoid Aggressive Driving, https://exchange.aaa.com/wp-content/uploads/2013/06/Road-Rage-Brochure.pdf. , 2022.03.18.
2
Blei D. M. (2012), Provavilistic topic models, Communications of the ACM, 55(4), 77-84. 10.1145/2133806.2133826
3
Choi S., Kim K., Kang J. (2020), An Exploratory study on Aggressive Driving and Road Rage in Korea, Korean Institute of Criminology, 1-460.
4
Chuang J., Manning C. D., Heer J. (2012), Termite: Visualization Techniques for Assessing Textual Topic Models, Advanced Visual Interfaces, 12, 74-77. 10.1145/2254556.2254572PMC3281944
5
Deliso M., “Disconcerting” Rise in Road Rage Shootings Resulting in Death or Injury, Data Shows, ABC News, https://abcnews.go.com/US/disconcerting-rise-road-rage-shootings-resulting-death-injury/story?id=78181165 , 2022.03.18.
6
Joo H. K. (2015), The Expansion of Special Criminal Laws in the Field of Road Traffic, Journal of Criminal Law, 27(4): 65, 173-202. 10.21795/kcla.2015.27.4.173
7
Kim N. G., Lee D. H., Choi H. C., Wong W. X. S. (2017), Investigations on Techniques and Applications of Text Analytics, The Journal of Korean Institute of Communications and Information Sciences, 42(2), 471-492. 10.7840/kics.2017.42.2.471
8
Kim S. K., Jang S. Y. (2016), A Study on the Research Trends in Domestic Industrial and Management Engineering using Topic Modeling, Journal of the Korea Management Engineers Society, 21(3), 71-95.
9
Kim T. G., Choi H. R., Lee H. C. (2016), A Study of the Research trends in fintech using topic modeling, Journal of the Korea Academia-Industrial Cooperation Society, 17(11), 670-681. 10.5762/KAIS.2016.17.11.670
10
Lee J. (2017), A Study on Road Rage: Focusing on Kyonggi Province, Master’s Thesis, Kyonggi University.
11
Lee J. Y., Park J. H., Yoon J. W., Lee S. B., Youm S. K., Lee Y. S., Yoon H. J. (2021), Analyzing Precedents for Sports Match Fixing by Applying Topic Modeling, Korean Society for Measurement and Evaluation in Physical Education and Sports Science, 23(2), 51-65.
12
Lee K. (2014), Study on Improvement of Apartment Housing Defect Judgment System Based in Judicial Precedent Cases, Doctoral dissertation, Hanyang University.
13
Mimno D., Wallach H., McCallum A. (2008), Gibbs Sampling for Logistic Normal Topic Models with Graph-based Priors, In NIPS Workshop on Analyzing Graphs, 1-8.
14
Ministry of Land, Infrastructure and Transport, Vehicle Registration Status, Statistics Korea, https://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=1257 , 2022.03.04.
15
National Police Agency, Driver’s License Holders Status, Statistics Korea, https://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=1617#quick_02; , 2022.03.04.
16
Park S. M., Lee H. P., So J. H., Yun I. S. (2021), Study of Analysis for Autonomous Vehicle Collision Using Text Embedding, The Journal of The Korea Institute of Intelligent Transport Systems, 20(1), 160-173. 10.12815/kits.2021.20.1.160
17
Ryu H. (2021), A Study on Road Rage: Focusing on Kyonggi Province, Master’s Thesis, Kyonggi University.
18
Yoo M., (Last year 2020), 5,536 Cases of Road Rage Incidents Increased by 25% Compared to the Previous Year, Dailynews, https://www.idailynews.co.kr/m/view.php?idx=76456 , 2022.03.15.
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 40
  • No :5
  • Pages :683-699
  • Received Date : 2022-08-05
  • Revised Date : 2022-08-10
  • Accepted Date : 2022-08-10
  • DOI :https://doi.org/10.7470/jkst.2022.40.5.683
Journal Informaiton Journal of Korean Society of Transportation Journal of Korean Society of Transportation
Archives
: 2015 Vol.34~
  • scopus
  • NRF
  • KOFST
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close