역사 맞춤형 정책 수립을 위한 지하철 역사의 유형화 및 특성 분석에 관한 연구: 서울 지하철 사례를 중심으로 (A Study on the Classification and Characterization Analysis Subway Station for the Customized Policies: Seoul Metropolitan Subway Cases)

서지정보

· 발행기관 : 한국인터넷전자상거래학회
· 수록지 정보 : 인터넷전자상거래연구 / 22권 / 4호 / 153 ~ 170페이지
· 저자명 : 김동언, LI QINGLONG, 김재경

초록

The Seoul metropolitan subway opened in 1974, and it became popular public transportation used by an average of 4.61 million passengers per day in 2021. Furthermore, by generating a vast amount of traffic data, it is showing value beyond public transportation. Developed countries utilize a combination of public transportation data and other types of data to efficiently develop public transportation operations and passenger-specific policies. On the other hand, despite various types of subway stations being added over time, the domestic study considers data of a single type (e.g., congestion, passenger, etc.). Such studies cannot effectively utilize traffic information and are challenging to utilize in various types of Seoul metropolitan subway. In this study, we aim to classify Seoul metropolitan subway from Line 1 to Line 9 by utilizing various data related to subway stations to suggest customized policies. The experiment results showed that the areas around the subway stations were categorized into three types: highly residential types, university·capital types, and transfer·commercial types. Our findings can provide appropriate policies for each type of subway station.

영어초록

The Seoul metropolitan subway opened in 1974, and it became popular public transportation used by an average of 4.61 million passengers per day in 2021. Furthermore, by generating a vast amount of traffic data, it is showing value beyond public transportation. Developed countries utilize a combination of public transportation data and other types of data to efficiently develop public transportation operations and passenger-specific policies. On the other hand, despite various types of subway stations being added over time, the domestic study considers data of a single type (e.g., congestion, passenger, etc.). Such studies cannot effectively utilize traffic information and are challenging to utilize in various types of Seoul metropolitan subway. In this study, we aim to classify Seoul metropolitan subway from Line 1 to Line 9 by utilizing various data related to subway stations to suggest customized policies. The experiment results showed that the areas around the subway stations were categorized into three types: highly residential types, university·capital types, and transfer·commercial types. Our findings can provide appropriate policies for each type of subway station.

참고자료

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