Browse > Article
http://dx.doi.org/10.5392/JKCA.2022.22.04.083

Crowdsourcing based Local Traffic Event Detection Scheme  

Kim, Yuna (충북대학교 빅데이터 협동과정)
Choi, Dojin (창원대학교 컴퓨터공학과)
Lim, Jongtae (충북대학교 정보통신공학부)
Kim, Sanghyeuk (충북대학교 빅데이터 협동과정)
Kim, Jonghun (충북대학교 정보통신공학부)
Bok, Kyoungsoo (원광대학교 인공지능융합학과)
Yoo, Jaesoo (충북대학교 정보통신공학부)
Publication Information
The Journal of the Korea Contents Association / v.22, no.4, 2022 , pp. 83-93 More about this Journal
Abstract
Research is underway to solve the traffic problem by using crowdsourcing, where drivers use their mobile devices to provide traffic information. If it is used for traffic event detection through crowdsourcing, the task of collecting related data is reduced, which lowers time cost and increases accuracy. In this paper, we propose a scheme to collect traffic-related data using crowdsourcing and to detect events affecting traffic through this. The proposed scheme uses machine learning algorithms for processing large amounts of data to determine the event type of the collected data. In addition, to find out the location where the event occurs, a keyword indicating the location is extracted from the collected data, and the administrative area of the keyword is returned. In this way, it is possible to resolve a location that is broadly defined in the existing location information or incorrect location information. Various performance evaluations are performed to prove the superiority and feasibility of the proposed scheme.
Keywords
Social Media; Machine Learning; Local Extraction; Traffic Event; Crowd Sourcing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 W. Y. Yang, et al, "Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine," Computers in biology and medicine, Vol.101, pp.22-32, 2018.   DOI
2 S. J. Huang, et al, "Applications of support vector machine (SVM) learning in cancer genomics," Cancer genomics & proteomics, Vol.15, No.1, pp.41-51, 2018.
3 Neruda, Gregorius Aria, and Edi Winarko. "Traffic Event Detection from Twitter Using a Combination of CNN and BERT," 2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS), IEEE, 2021.
4 https://aiopen.etri.re.kr
5 정한유, "모바일 크라우드소싱 기반 운전자 지원 시스템의 설계 및 구현," 전기전자학회논문지, 제22권, 제1호, pp.29-37, 2018.   DOI
6 S. Klaithin and C. Haruechaiyasak, "Traffic information extraction and classification from Thai Twitter," Proc. International Joint Conference on Computer Science and Software Engineering, pp.1-6, 2016.
7 L. Arp, D. Vreumingen, D. Gawehns, and M. Baratchi, "Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data," Proc. IEEE International Conference on Mobile Data Management, pp.168-177, 2020.
8 https://www.index.go.kr
9 박범진, 문병섭, 변장선, "크라우드 소싱의 ITS 적용방안," 한국ITS 학회논문지, 제11권, 제2호, pp.48-56, 2012.
10 D. Vij and N. Aggarwal, "Smartphone based traffic state detection using acoustic analysis and crowdsourcing," Applied Acoustics, Vol.138, pp.80-91, 2018.   DOI
11 https://www.its.go.kr
12 Z. Xu, Y. Liu, N. Y. Yen, L. Mei, X. Luo, X. Wei, and C. Hu, "Crowdsourcing Based Description of Urban Emergency Events Using Social Media Big Data," IEEE Transactions on Cloud Computing, Vol.8, No.2, pp.387-397, 2020.   DOI
13 Alomari, Ebtesam, Rashid Mehmood, and Iyad Katib, "Sentiment analysis of Arabic tweets for road traffic congestion and event detection," Smart Infrastructure and Applications. Springer, Cham, pp.37-54, 2020.
14 S. Zhang, Y. Cheng, and D. Ke, "Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams," arXiv:1708.05878, pp.1-10, 2017.
15 S. Klaithin and C. Haruechaiyasak, "Traffic information extraction and classification from Thai Twitter," Proc. International Joint Conference on Computer Science and Software Engineering, pp.1-6, 2016.
16 E. A. Alomari, I. A. Katib, A. Albeshri, T. Yigitcanlar, and R. Mehmood, "Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning," Sensors, Vol.21, No.9, pp.1-33, 2021.   DOI
17 https://developers.google.com/maps
18 https://developers.kakao.com
19 http://www.tbn.or.kr
20 Norton, Edward C., Bryan E. Dowd, and Matthew L. Maciejewski, "Marginal effects-quantifying the effect of changes in risk factors in logistic regression models," Jama, Vol.321, No.13, pp.1304-1305, 2019.   DOI
21 C. Zhang, L. Liu, D. Lei, Q. Yuan, H. Zhuang, T. Hanratty, and J. Han, "Triovecevent: Embedding-based online local event detection in geo-tagged tweet streams," Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.595-604, 2017.
22 D. Berrar, "Bayes' theorem and naive Bayes classifier," Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 403, 2018.
23 T. Sakaki, Y. Matsuo, T. Yanagihara, N. P. Chandrasiri, and K. Nawa, "Real-time event extraction for driving information from social sensors," Proc. IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, pp.221-226, 2012.