Factors generating boardings at Metro stations in the Seoul metropolitan area

Sohn, Keemin; Shim, Hyunjin

doi:10.1016/j.cities.2010.05.001

Cited by 188 publications

(161 citation statements)

References 21 publications

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“…Meanwhile, it has been accepted that better understanding the first/lastmile home-bound trip stages is useful for transport modeling, infrastructure planning, urban design, and health research communities (Clifton and Muhs 2012). The common sense that distance has a steeper negative effect on the choice of walking and cycling as compared to motorized modes has been demonstrated in many studies (Debrezion et al 2009;Sohn and Shim 2010;Wardman and Tyler 2010). In addition to distance, research has been carried out on the characteristics of the first/last-mile trip stages with respect to time and facility attributes (Hine and Scott 2000;Kuby et al 2004;Guo and Wilson 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice

Meng¹,

Koh²,

Wong³

2016

JPT

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Section: Literature Reviewmentioning

confidence: 99%

Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice

Meng¹,

Koh²,

Wong³

2016

JPT

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“…The main objective of the study is classifying the stations according to the balance between offers of transportation and activities developed in its area. This guideline is identified in other several studies (Gutierrez et al, 2011;Maghelal, 2011;Sohn and Shim, 2010;Kim et al, 2007;Kuby et al, 2004), all of them aiming to detect the variables that can influence ridership. These studies have been carried out in different historical, cultural, social and political contexts.…”

Section: Introductionmentioning

confidence: 90%

“…In order to identify and analyze the factors the influence the boarding and landing rates of the railways stations, a bibliographic review was carried out, from Gonçalves et al (2012), contemplating the following studies: Brown et al 2013;Frei and Mahmassani, 2013;Basu and Hunt, 2012;Gutiérrez et al, 2011;Lucas, 2011;Sohn and Shim, 2010;Debrezion et al, 2009;Brons et al, 2008;Anspacher, 2005;Chu, 2004;Kuby et al, 2004;Walters andCervero, 2003 andQuade andDouglas, 1996. It is worth mentioning that Basu and Hunt (2012), trough the performance of the study in the case of Mumbai, India, along with Gonçalves et al (2012) are the only ones focused on developing countries.…”

Section: Factors Variables and Protagonists Of Rail Travel Ridershipmentioning

confidence: 99%

Analysis of Explanatory Variables of Rail Ridership: The Situation of Rio de Janeiro

Andrade

Gonçalves

Portugal

2014

Procedia - Social and Behavioral Sciences

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This paper aims to establish, from the variables incorporated in the bibliography, the ones potentially indicated to explain and rate the rail ridership, considering the characteristics and specificities of the suburban trains and stations of Rio de Janeiro Metropolitan Area (RJMA) as well as its environment. It will be considered the data of railway branch lines of RJMA and their links, along with the econometric models that will be determined, taking into account the explanatory variables of boarding and landing rates of train stations. Contrary to what is observed in international literature, the HDI (Human Development Index), average income and population variables present low interrelation with the following dependent variables: boarding rate, landing rate and total amount of journeys. The HDI calls our attention because it presents a negative interrelation, which strengths the idea that in Brazil the train offer does not contribute to a human development. The low correlation with the average income and population may explain the low quality of the service and the unattractive image of the train. The variables that present greater explanatory capacity of railway rates were the number of jobs in the area of influence and the numbers of extensions of the train itself. This suggests that in Rio the train plays the role of the transport that will take the employees to their workplaces, highlighting the importance of connectivity to obtain passengers.

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“…Seoul is the sixth most densely populated city in the world, and, as a consequence, compact development has already taken place in the vicinity of its station areas. It should be noted that the current population, or employment density, within station areas in Seoul is much higher than the longterm goal levels that new urbanists in the western world have advocated (Sohn and Shim, 2010). Nonetheless, the density level within the station areas must be differentiated from other areas, because altering the urban form of Seoul to suppress automobile use would help to ensure sustainable lives for residents in the future.…”

Section: Introductionmentioning

confidence: 98%

“…In the previous studies (Sohn and Shim, 2010;Choi et al, 2011) that motivated the present study, variables characterizing each station area were already chosen with respect to its built environments, demographic characteristics, socioeconomic status, and transport aspects, and the inter-relationships between them were investigated in a rigorous manner based on econometric models such as the structural equation model (SEM), the multiple regression model (MRA), and the multiplicative spatial interaction model. More recently in the same context, Lee and Sohn (2013) investigated the difference in land values depending on whether each station area served underground subways or at-grade railways.…”

Section: Introductionmentioning

confidence: 99%

Feature Mapping the Seoul Metro Station Areas Based on a Self-Organizing Map

Sohn¹

2013

Journal of Urban Technology

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Land in the vicinity of railway stations in the Seoul metropolitan area has been spotlighted as a target for redevelopment in accordance with the principles of transit-oriented development (TOD). In order to understand the nature of station areas as a whole, it is required to identify their current status with respect to their built environments, demographic characteristics, socioeconomic status, and transport aspects. Most of the previous studies that have focused on characterizing urban areas assumed that clearly separable clustering is possible and tried to find a robust methodology for that. Many researchers have used either supervised classifiers requiring a pre-classified dataset for training, or K-means-like unsupervised classifiers demanding a predetermined number of clusters. The present study focused on the fact that it was hard to find such a clear separation in station areas in Seoul. A more abstract technology was necessary to position the current status of each station area and to find how different station areas are from one another. A robust unsupervised classifier, called the self-organizing map (SOM), was employed to investigate the similarities and differences among station areas in Seoul. The SOM results revealed many informative findings for policy development without any classification.

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Factors generating boardings at Metro stations in the Seoul metropolitan area

Cited by 188 publications

References 21 publications

Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice

Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice

Analysis of Explanatory Variables of Rail Ridership: The Situation of Rio de Janeiro

Feature Mapping the Seoul Metro Station Areas Based on a Self-Organizing Map

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