기계학습 클러스터링을 이용한 승하차 패턴에 따른 서울시 지하철역 분류 = Classification of Seoul Metro Stations Based on Boarding/ Alighting Patterns Using Machine Learning Clustering

다국어 초록 (Multilingual Abstract)

In this study, we classify Seoul metro stations according to boarding and alighting patterns using machine earning technique. The target data is the number of boarding and alighting passengers per hour every day at 233 subway stations from 2008 to 2017 provided by the public data portal. Gaussian mixture model (GMM) and K-means clustering are used as machine learning techniques in order to classify subway stations. The distribution of the boarding time and the alighting time of the passengers can be modeled by the Gaussian mixture model. K-means clustering algorithm is used for unsupervised learning based on the data obtained by GMM modeling. As a result of the research, Seoul metro stations are classified into four groups according to boarding and alighting patterns. The results of this study can be utilized as a basic knowledge for analyzing the characteristics of Seoul subway stations and analyzing it economically, socially and culturally. The method of this research can be applied to public data and big data in areas requiring clustering.

참고문헌 (Reference)

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5 Minwoo Kim, "Predicting Subway Passengers Flows By Spatio-Temporal Modeling" Seoul National University 2017

6 R. S. Michalski, "Machine Learning: An Artificial Intelligence Approach," Springer Science & Business Media 2013

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