유동 클러스터 탐지 기법의 개발과 적용: 서울시 대중교통 통행을 사례로

Abstract

Smart-card data is a high quality data that can be used in analyzing urban flow patterns. However, due to methodological difficulties of flow clustering, earlier studies have not been able to take full advantage of the smart-card data. To overcome this limitation, this study introduces an efficient flow cluster detection method based on smart-card data of public transportation and applies it to public transit trips in Seoul to derive significant flow clusters of trips. As a result of case study, we identified various clusters of public trasit trips in Seoul and could detect significant clusters that are composed of several individual travel flows being adjacent in space but mostly out of different transport modes and routes. Furthermore, the methodology of this study made it possible to capture the major travel flow patterns. They have not been recognized as important flow patterns in a normal way, but they have significant traffic volume when gathered into a flow cluster. This study provides important methodological implications to the analysis of flow patterns, and the suggested algorithm is expected to be the basis of more advanced flow clustering techniques in the future.