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http://dx.doi.org/10.12815/kits.2021.20.4.57

An Analysis of Civil Complaints about Traffic Policing Using the LDA Model  

Lee, Sangyub (Dept. of Police Science, Korea National Police University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.4, 2021 , pp. 57-70 More about this Journal
Abstract
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.
Keywords
Traffic Policing; Civil Complaint; Topic Modeling; Latent Dirichlet Allocation(LDA); e-People;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 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.   DOI
2 Roder 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.
3 Shin A.(2019), Keyword and Topic Analysis on Free Semester Policy Using Big Data, Ph.D. Dissertation, The Graduate School of Seoul National University.
4 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.
5 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.   DOI
6 Yonhap News Agencies, https://news.naver.com/main/read.nhn?mode=LSD&mid=sec&sid1=102&oid=001&aid=0006801892, 2021.07.02.
7 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.
8 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.
9 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.   DOI
10 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.   DOI
11 Korea Legislation Research Institute, https://elaw.klri.re.kr/kor_service/main.do, 2021.07.05.
12 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.   DOI
13 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.   DOI
14 Ministry of Government Legislation, https://www.moleg.go.kr/lawinfo/makingInfo.mo?lawSeq=60649&lawCd=0&&lawType=TYPE5&mid=a10104010000, 2021.07.05.
15 Kim S.(2009), "An Analysis of Policing Needs in Daegu Gyeongbuk Areas," Korean Local Government Review, vol. 11, no. 3, pp.185-203.
16 Korea Information Society Agency, https://www.data.go.kr/, 2021.06.15.
17 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.   DOI
18 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.
19 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.   DOI
20 Medical World News, http://medicalworldnews.co.kr/news/view.php?idx=1510929258, 2021.06.21.
21 Moneytoday, https://news.mt.co.kr/mtview.php?no=2020091716432163577, 2021.07.02.
22 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.   DOI
23 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.
24 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.
25 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.   DOI
26 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.
27 Hamzeian D.(2021), Using Machine Learning Algorithms for Finding the Topics of COVID-19 Open Research Dataset Automatically, Master's Thesis, University of Waterloo.
28 Blei D. M.(2012), "Probabilistic Topic Models," Communications of the ACM, vol. 55, no. 4, pp.77-84.   DOI
29 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.
30 Choi S. and Ko E.(2019), "Analysis of from 1960 to 2018 using Metadata with Dynamic Topic Modeling," Korean Journal of Journalism & Communication Studies, vol. 63, no. 4, pp.7-42.   DOI
31 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.
32 Kil H.(2018), "The Study of Korean Stopwords list for Text mining," URIMALGEUL: The Korean Language and Literature, vol. 78, pp.1-25.   DOI
33 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.
34 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.
35 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.   DOI