Urban structural hierarchy and the relationship between the ridership of the Seoul Metropolitan Subway and the land-use pattern of the station areas

Lee, Seungil; Yi, Changhyo; Hong, Soung-Pyo

doi:10.1016/j.cities.2013.06.010

Cited by 64 publications

(28 citation statements)

References 15 publications

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“…Seoul has been a Transit-Oriented Metropolis (TOM) since the 2000s, when many highdensity and mixed-use developments were completed, along with the public transportation networks [31]. The continuous efforts of TOD planning have been executed by integrating station area developments with transit networks, the housing supply, other facilities, and land-use patterns since the Comprehensive Plan.…”

Section: Development Of Subway Tod Planning In Seoulmentioning

confidence: 99%

“…The research areas are set actual pedestrian network coverages within 500 m of the 246 targeted stations in Seoul. The usual range of walkable distance to stations is 600 to 1000 m [33]; however, many Korean scholars use 500 m as the ideal walking distance to stations due to the usual travel time of 10 min [31]. The designated 500 m walkshed is defined as a subway TOD area focusing on pedestrian accessibility in this research.…”

Section: Description Of the Study Area And Datasetmentioning

confidence: 99%

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Classification of TOD Typologies Based on Pedestrian Behavior for Sustainable and Active Urban Growth in Seoul

Woo

2021

Sustainability

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Transit-oriented development (TOD) pursues sustainable urban development through compact growth, mixed-use zoning, and pedestrian-friendly neighborhood design in cooperation with transportation planning. Seoul has actively developed urban rail transit since the 1970s based on a TOD concept, and each station’s areas have differently evolved throughout the history of urbanization in Seoul. In response to investigating the complications of current TOD, this paper evaluates TOD characteristics through accessibility and clustering analysis methods and categorizes TOD types using the targeted 246 subway station areas at the neighborhood level. As a result, subway TODs are grouped into the four distinct categories of (1) high-density: a form of mainly mixed-use with residential and retail development and good accessibility; (2) moderate-density: average accessibility and high-mixed use; (3) compact business district setting: highly accessible to offices and retail; and (4) compact housing: high-rise apartments with schools and retail. The results also find that Cluster 2 is the most common TOD type and redevelopment possibility in Seoul, with relatively lower ranks in the building floor area (GFA) and diversity in comparison to other TOD contexts. Cluster 3 has the most significant transit demand, generating an active transit environment in Seoul. Different urban development periods impact the characteristics of TOD types.

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Section: Development Of Subway Tod Planning In Seoulmentioning

confidence: 99%

Section: Description Of the Study Area And Datasetmentioning

confidence: 99%

Classification of TOD Typologies Based on Pedestrian Behavior for Sustainable and Active Urban Growth in Seoul

Woo

2021

Sustainability

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“…Massive POI data could fully reflect urban functions and greatly improve the explanatory power in modeling impacts of built environment on travel behaviors. Considering the pedestrian-friendly transit network in the planning of TOD, the radius of a pedestrian catchment area (PCA) was set to 500 m in many previous studies [37,40,41]. A PCA is generally defined as a geographical circle area where a great majority of pedestrians arrive on foot.…”

Section: Data Sourcementioning

confidence: 99%

Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances

Chen

2019

Sustainability

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The primary objective of this study is to explore spatio-temporal effects of the built environment on station-based travel distances through large-scale data processing. Previous studies mainly used global models in the causal analysis, but spatial and temporal autocorrelation and heterogeneity issues among research zones have not been sufficiently addressed. A framework integrating geographically and temporally weighted regression (GTWR) and the Shannon entropy index (SEI) was thus proposed to investigate the spatio-temporal relationship between travel behaviors and built environment. An empirical study was conducted in Nanjing, China, by incorporating smart card data with metro route data and built environment data. Comparative results show GTWR had a better performance of goodness-of-fit and achieved more accurate predictions, compared to traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR). The spatio-temporal relationship between travel distances and built environment was further analyzed by visualizing the average variation of local coefficients distributions. Effects of built environment variables on metro travel distances were heterogeneous over space and time. Non-commuting activity and exurban area generally had more influences on the heterogeneity of travel distances. The proposed framework can address the issue of spatio-temporal autocorrelation and enhance our understanding of impacts of built environment on travel behaviors, which provides useful guidance for transit agencies and planning departments to implement targeted investment policies and enhance public transit services.

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“…Many studies have built upon the idea of PCA, discussing acceptable walking times and speeds in different build environments and transit trips [12][13][14]. For predicting travel demand such as ridership, variables including land use pattern, transit accessibility, and demographic background are often regarded as robust predictors [15][16][17]. However, even in the same city, significant differences of CA results exist because of predictor selection, analysis method and model specification [3,4,18].…”

Section: Research Related To Catchment Areasmentioning

confidence: 99%

Correlation between Transit-Oriented Development (TOD), Land Use Catchment Areas, and Local Environmental Transformation

et al. 2018

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Transit-oriented development (TOD) has been recognised as a sustainable planning approach and that is typically designed for a whole city. Individual land use characteristics and the causations have often been ignored. Therefore, the primary objective of this study was to explore the factors that influence the land use catchment area (LCA) characteristics at a station neighborhood level. First, it contributes a methodology to measure the LCA by introducing a new concept. The density gradient was introduced to generate the scale and compactness degree of each station. Second, it provides a theoretical framework for understanding the causes of different LCAs. The partial least squares (PLS) regression model was employed to explore the accessibility effects. By analysing density gradient curves, it reveals that stations grew to fit the negative exponential function. Regarding the scale and form degree of LCAs, the impact of accessibility before and after a station construction have been corroborated. Moreover, the effects of facilities function before construction, distance from main roads, and elevated stations have been emphasized. The results provide support for a more sophisticated concept of catchment area relating to land use at the level of an individual TOD station, while shedding light on the benefits of those engaged in the future design of TOD with due consideration of the local physical environments.

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Urban structural hierarchy and the relationship between the ridership of the Seoul Metropolitan Subway and the land-use pattern of the station areas

Cited by 64 publications

References 15 publications

Classification of TOD Typologies Based on Pedestrian Behavior for Sustainable and Active Urban Growth in Seoul

Classification of TOD Typologies Based on Pedestrian Behavior for Sustainable and Active Urban Growth in Seoul

Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances

Correlation between Transit-Oriented Development (TOD), Land Use Catchment Areas, and Local Environmental Transformation

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