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본 연구는 민원데이터를 분석함으로써 교통경찰에 대한 국민의 치안 수요를 탐색하고자 하였다. 이를 위해 교통경찰 관련 국민신문고 민원데이터 2,062건을 대상으로, 토픽모델링 방법 중 하나인 잠재 디리클레 할당(Latent Dirichlet Allocation)을 통해 주요 토픽을 추출하고 높은 비중을 차지한 위반신고에 대해 추가분석을 시도하였다. 이 과정에서 키워드와 대표문서의 일관성과 합치성을 함께 고려하였다. 분석 결과 교통경찰 관련 민원은 시설개선, 신호에 따른 교차로통행방법, 번호판 영치, 개인형 이동장치 등 41개의 토픽으로 분류할 수 있었다. 교차로 내 위반과 이륜자동차의 위반에 대한 단속을 강화하고 무인교통단속장비, 횡단보도, 신호등의설치 및 운영에 대한 선제적인 조치, 최근 개정된 법령과 시행된 정책, 경찰교통민원 사이트, 단속 사후 절차에 대한 더욱 활발한 홍보가 필요한 것으로 판단된다.

This study aims to investigate the security demand about the traffic policing by analyzing civil complaints. Latent Dirichlet Allocation(LDA) was applied to extract key topics for 2,062 civil complaints data related to traffic policing from e-People. And additional analysis was made of reports of violations, which accounted for a high proportion. In this process, the consistency and convergence of keywords and representative documents were considered together. As a result of the analysis, complaints related to traffic police could be classified into 41 topics, including traffic safety facilities, passing through intersections(signals), provisional impoundment of vehicle plate, and personal mobility. It is necessary to strengthen crackdowns on violations at intersections and violations of motorcycles and take preemptive measures for the installation and operation of unmanned traffic control equipments, crosswalks, and traffic lights. In addition, it is necessary to publicize the recently amended laws a implemented policies, e-fine, procedure after crackdown.

권호기사

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기사명 저자명 페이지 원문 목차
XGBoost를 활용한 이륜자동차 교통사고 심각도 비교분석 = Comparative analysis of traffic accident severity of two-wheeled vehicles using XGBoost 권철우, 장현호 p. 1-12

IDM을 이용한 자율주행자동차 시장점유율 변화가 고속도로 교통류에 미치는 영향 분석 = Analysis of effects of autonomous vehicle market share changes on expressway traffic flow using IDM 고우리, 박상민, 소재현, 윤일수 p. 13-27

국내외 자율주행차 테스트베드 분석 기반 K-City 발전 전략 수립에 관한 연구 = Study on establishment of development strategy for K-city based on analysis of domestic and overseas automated vehicle testbeds 김예진, 박상민, 김인영, 고한검, 조성우, 윤일수 p. 28-45

돌발상황 처리시간 예측을 위한 영향요인 분석 및 SMOGN-DNN 모델 개발 = Analysis of incident impact factors and development of SMOGN-DNN model for prediction of incident clearance time 윤규리, 배상훈 p. 46-56

토픽모델링을 활용한 교통경찰 민원 분석 = An analysis of civil complaints about traffic policing using the lda model 이상엽 p. 57-70

도로 자산관리를 위한 상태 모니터링 및 경제성 분석 : Infrastructure health monitoring and economic analysis for road asset management : focused on Sejong City / 세종시를 중심으로 최승현, 박정권, 도명식 p. 71-82

GBAS용 3중 폴디드 무지향성 마이크로스트립 안테나 = Triple folded omnidirectional microstrip antenna for GBAS 주대근, 우종명 p. 83-94

적대적 학습을 이용한 도로 노면 파손 탐지 알고리즘 = Detection algorithm of road surface damage using adversarial learning 심승보 p. 95-105

교통류에 영향을 주는 화물차 군집주행 운영 조건 분석 = Analysis of truck platooning operation conditions affecting traffic flow 정하림, 이영택, 박상민, 조현배, 윤일수 p. 106-117

참고문헌 (35건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Baeck B.(2013), “A Study on the Administrative System and Problems ‘Voice of Customer’,” The Korean Association of Police Science Review, vol. 15, no. 5, pp.115-146. 미소장
2 Baek S.(2019), Exploration on utilization of word embedding for topic modeling in Korean data, Master’s Thesis, The Graduate School of Seoul National University. 미소장
3 Blei D. M.(2012), “Probabilistic Topic Models,” Communications of the ACM, vol. 55, no. 4, pp.77-84. 미소장
4 Blei D. M., Ng A. Y., and Jordan M. I.(2003), “Latent dirichlet allocation,” The Journal of Machine Learning Research, vol. 3, pp.993-1022. 미소장
5 Choi H.(2016), “Study on Selecting Priority Criteria Utilizing Civil Complaint Data in the Field of Environment and Sanitation,” Journal of Environmental Policy and Administration, vol, 24, no. 2, pp.45-57. 미소장
6 Choi S., and Ko E.(2019), “Analysis of 〈Korean Journal of Journalism & Communication Studies〉 from 1960 to 2018 using Metadata with Dynamic Topic Modeling,” Korean Journal of Journalism & Communication Studies, vol. 63, no. 4, pp.7-42. 미소장
7 Hamzeian D.(2021), Using Machine Learning Algorithms for Finding the Topics of COVID-19 Open Research Dataset Automatically, Master's Thesis, University of Waterloo. 미소장
8 Hong S., Jeong I., and Lee Y.(2019), “A Study on the Automatic Categorization of Security Demand through Text Mining,” The Journal of Police Science, vol. 19, no. 2, pp.271-298. 미소장
9 Kang M.(2009), “Traffic Accident analysis and Traffic Police Activity-Centering Gwangju Province Police Agency,” Journal of the Korea Contents Association, vol. 9, no. 9, pp.199-209. 미소장
10 Kil H.(2018), “The Study of Korean Stopwords list for Text mining,” URIMALGEUL: The Korean Language and Literature, vol. 78, pp.1-25. 미소장
11 Kim C., Kang J., and Park J.(2019), “A Study on the opinion spam detection system using natural language processing based on machine Learning,” Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, pp.956-957. 미소장
12 Kim J. Y., and Chang J. S.(2018), “Analysing Civil Traffic Complaints using Latent Dirichlet Allocation,” Proceedings of the KOR-KST Conference, vol. 79, pp.106-111. 미소장
13 Kim S.(2009), “An Analysis of Policing Needs in Daegu‧Gyeongbuk Areas,” Korean Local Government Review, vol. 11, no. 3, pp.185-203. 미소장
14 Korea Information Society Agency, https://www.data.go.kr/, 2021.06.15. 미소장
15 Korea Legislation Research Institute, https://elaw.klri.re.kr/kor_service/main.do, 2021.07.05. 미소장
16 Lee H., Chang J., and Kim G.(2020), “A Study on the Conflict Structure of the Standing Committee through Topic Analysis of the National Assembly Minutes: Health and Welfare Committee in the First Half of the 20th National Assembly,” Korean Party Studies Review, vol. 19, no. 2, pp.131-167. 미소장
17 Lee S., and Kim H.(2009), “Keyword Extraction from News Corpus using Modified TF-IDF,” The Journal of Society for e-Business Studies, vol. 14, no. 4, pp.59-73. 미소장
18 Lee Y., Lee Y., Seong J., Stanescu A., Ji S., and Hwang C. S.(2020), “An Analysis of the latest Trends and Topics in Geography Research Using Topic Modeling,” Journal of the Korean Geographical Society, vol. 55, no. 6, pp.589-599. 미소장
19 Medical World News, http://medicalworldnews.co.kr/news/view.php?idx=1510929258, 2021.06.21. 미소장
20 Ministry of Government Legislation, https://www.moleg.go.kr/lawinfo/makingInfo.mo?lawSeq=60649&lawCd=0&&lawType=TYPE5&mid=a10104010000, 2021.07.05. 미소장
21 Moneytoday, https://news.mt.co.kr/mtview.php?no=2020091716432163577, 2021.07.02. 미소장
22 Na S., Yang G., and Shin J.(2019), “Classifying Customer’s Written Questions by Topics Using Deep Learning(LSTM),” The Transactions of the Korean Institute of Electrical Engineers, vol. 68, no. 11, pp.1411-1416. 미소장
23 Oh C., Lee Y., and Ko M.(2016), “Establishment of ITS Policy Issues Investigation Method in the Road Section applied Text mining,” The Journal of the Korea Institute of Intelligent Transport Systems, vol. 15, no. 6, pp.10-23. 미소장
24 Park H., Kim H., and Hong Y.(2017), “A Topic Modeling Analysis on the Major Social Issues of the Students’ Human Rights Ordinance in Korea,” Asian Journal of Education, vol. 18, no. 4, pp.683-711. 미소장
25 Park J., and Lee S.(2020), “Big Data Analysis of Busan Civil Affairs Using the LDA Topic Modeling Technique,” Information Policy, vol. 27, no. 2, pp.66-83. 미소장
26 Park S., Lee H., So J., and Yun I.(2021), “Study of Analysis for Autonomous Vehicle Collision Using Text Embedding,” The Journal of the Korea Institute of Intelligent Transport Systems, vol. 20, no. 1, pp.160-173. 미소장
27 Röder M., Both A., and Hinneburg A.(2015), “Exploring the space of topic coherence measures,” In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp.399-408. 미소장
28 Shin A.(2019), Keyword and Topic Analysis on Free Semester Policy Using Big Data, Ph.D. Dissertation, The Graduate School of Seoul National University. 미소장
29 Syed S., and Spruit M.(2017), “Full-text or abstract? Examining topic coherence scores using latent dirichlet allocation,” In 2017 IEEE International Conference on Data Science and Advanced Analytics(DSAA), pp.165-174. 미소장
30 Woo C. W., and Lee J. Y.(2020), “Investigation of Research Topic and Trends of National ICT Research-Development Using the LDA Model,” Journal of the Korea Convergence Society, vol. 11, no. 7, pp.9-18. 미소장
31 Woo Y. H., and Kim H. H.(2020), “Topic Analysis of the National Petition Site and Prediction of Answerable Petitions Based on Deep Learning,” KIPS Transactions on Software and Data Engineering, vol. 9, no. 2, pp.45-52. 미소장
32 Yang H., Ahn J., and Lee T.(2021), “A Study of Korean's Experiences of Unfairness Based on Analysis of Text Big Data Posted on the Blue House National Petition,” Survey Research, vol. 22, pp.25-59. 미소장
33 Yonhap News Agencies, https://news.naver.com/main/read.nhn?mode=LSD&mid=sec&sid1=102&oid=001&aid=0006801892, 2021.07.02. 미소장
34 Yoo S., Kang B., Kim J., Lee G., Lee M., and Koh S.(2020), “The Lowest Price Matching Service Using Cosine Similarity Analysis,” Proceedings of the Korean Society of Broadcast Engineers Conference, pp.502-507. 미소장
35 Yu Y. R.(2017), Analysis of media coverage on 2015 revised curriculum policy using Big Data Analysis, Ph.D. Dissertation, The Graduate School of Seoul National University. 미소장

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