• Title/Summary/Keyword: diurnal patterns of transit ridership

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Relationship between Diurnal Patterns of Passenger Ridership and Passenger Trip Chains on the Metropolitan Seoul Metro System (수도권 광역도시철도 하루 시간대별 이용 빈도에 의해 구분된 역 집단과 통행자의 통행 연쇄 패턴 간 관계)

  • Lee, Keum-Sook;Park, Jong-Sook;Kim, Ho-Sung;Joh, Chang-Hyeon
    • Journal of the Korean Geographical Society
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    • v.45 no.5
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    • pp.592-608
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    • 2010
  • This study investigates the diurnal pattern of transit ridership in the Metropolitan Seoul area. For the purpose, we use a weekday Smart Card passenger transaction data in 2005. Eleven passenger trip patterns are found from 2.74 million passengers moving on the Metropolitan Seoul Metro system. Among them, we analyze 2.4 million passengers blonging to five trip types having only one or two transaction record during a day. A total of 357 metro stations are classified to four types according to their diurnal pattern of passenger riderships. We analyze the relationships between passenger's trip chain patterns and subway station's diurnal transit ridership patterns. The result shows that the ratio of the number of passengers of particular time of the day is hierarchically related with trip chain patterns.

Relationship between Diurnal Patterns of Transit Ridership and Land Use in the Metropolitan Seoul Area (서울 대도시권 하루 시간대별 지하철 통행흐름 패턴과 토지이용과의 관계)

  • Lee, Keum-Sook;Song, Ye-Na;Park, Jong-Soo;Anderson, William P.
    • Journal of the Economic Geographical Society of Korea
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    • v.15 no.1
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    • pp.26-41
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    • 2012
  • This study investigates the time-space characteristics of intra-urban passenger flows in the Metropolitan Seoul area. In particular, we analyze the relationships between transit ridership and land use through the use of the subway passenger flow data obtained from the transit transaction databases. For this purpose, the strength of each subway station, i.e., the number of total in-coming and out-going passengers at each station, in the morning, afternoon, and evening, is calculated and visualized, which reflects urban land use patterns. Then the subway stations are classified into four groups via a hierarchical analysis of the in-coming and out-going passenger flows at 353 stations. Each group appears to have characteristic properties according to the region, e.g., residential areas and central business districts. This has been confirmed by the analysis which probes explicitly the relationship between the local socio-economic variables and station groups. This analysis, disclosing the inter-relationship between the subway network and urban land use, may be useful at various stages in urban as well as transportation planning, and provides analytical tools for a wide spectrum of applications ranging from impact evaluation to decision-making and planning support.

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