Browse > Article
http://dx.doi.org/10.14400/JDC.2016.14.11.289

Subway Congestion Prediction and Recommendation System using Big Data Analysis  

Kim, Jin-su (College of Liberal Arts, Anyang University)
Publication Information
Journal of Digital Convergence / v.14, no.11, 2016 , pp. 289-295 More about this Journal
Abstract
Subway is a future-oriented means of transportation that can be safely and quickly mass transport many passengers than buses and taxis. Congestion growth due to the increase of the metro users is one of the factors that hinder citizens' rights to comfortably use the subway. Accordingly, congestion prediction in the subway is one of the ways to maximize the use of passenger convenience and comfort. In this paper, we monitor the level of congestion in real time via the existing congestion on the metro using multiple regression analysis and big data processing, as well as their departure station and arrival station information More information about the transfer stations offer a personalized congestion prediction system. The accuracy of the predicted congestion shows about 81% accuracy, which is compared to the real congestion. In this paper, the proposed prediction and recommendation application will be a help to prediction of subway congestion and user convenience.
Keywords
Congestion Prediction; Big Data; Multiple Regression Analysis; Congestion Prediction Application; Personal Preference;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Jung-Hoon Kim, Jun-Young Go, Keun-Ho Lee, "A Scheme of Social Engineering Attacks and Countermeasures Using Big Data based Conversion Voice Phishing", Journal of the Korea Convergence Society, Vol. 6, No. 1, pp. 85-91, 2015.   DOI
2 Keun-Won Kim, Dong-Woo Kim, Kyoo-Sung Noh, Joo-Yeoun Lee, "An Exploratory Study on Improvement Method of the Subway Congestion Based Big Data Convergence", Journal of Digital Convergence, Vol.13, No.2, pp.35-42, 2015.   DOI
3 Rencher, Alvin C., Christensen, William F., Methods of Multivariate Analysis, Wiley Series in Probability and Statistics, 709 (3rd ed.), John Wiley & Sons, 2012.
4 http://kosis.kr/
5 Hee-Seog Koh, A Study of the train operating efficiency - Incheon Subway Line 1 to attract utilizes the center, Korea National University of Transportation, 2015.
6 Yong-Hyun Cho, "Metropolitan commuting time in half", Koera Railroad Research Institute, 2013.
7 Keun-Won Kim, Dong-Woo Kim, Kyoo-Sung Noh, Joo-Yeoun Lee, "An Exploratory Study on Improvement Method of the Subway Congestion Based Big Data Convergence", JOURNAL OF DIGITAL CONVERGENCE, Vol.13, No.2, pp.35-42, 2015   DOI
8 Seon-Ha Lee, Choon-Keun Cheon, Byung-Doo Jung, Byung-Young Yu, Eun-Ji Kim, "Study on Methodology for Effect Evaluation of Information Offering to Rail passengers - Focusing on the Gate Metering Case Study considering congested conditions at a platform -", The Korea Institute of Intelligent Transport Systems, Vol.14, No.3, pp.50-62, 2015
9 Jong-Hyung Kim, Jhi-Eon Sohn, The Congestion Index of Urban Rail for the Transportation Welfare in Incheon, Incheon Development Institute, 2014.
10 Mi-Young Bin el al., Study on Rail Construction Feasibility by Considering Transportation Welfare, Gyeonggi Research Institute, 2012.
11 http://www.ictr.or.kr
12 Kyoung-Ho Choi, Jin-Ah Yoo, "A reviews on the social network analysis using R", Journal of the Korea Convergence Society, Vol. 6, No. 1, pp. 77-83, 2015.   DOI
13 James Manyika, Michael Chui, "Big data: The next frontier for innovation, competition, and productivity", McKinsey Global Institute, May 2011.
14 Seung-Kirl Baek, Dong-Joo Park, "A Status and Policy Direction of Transportation Welfare in Interregional Transportation", Transportation technology and policy, v.9, no.5, pp.52-60. 2012.
15 John Gantz, David Reinsel, "Extracting Value from Chaos", IDC IVIEW, June 2011.
16 Jeong-Mee Lee, "Understanding Big Data and Utilizing its Analysis into Library and Information Services", Journal of the Korean Biblia Society For Library And Information Science Vol. 24, No. 4, pp. 53-73, 2013.