Ridership patterns at subway stations of Seoul capital area and characteristics of station influence area

Kim, Mi Kyeong; Kim, Sang Pil; Heo, Joon; Sohn, Hong Gyoo

doi:10.1007/s12205-016-1099-8

Cited by 32 publications

(18 citation statements)

References 11 publications

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“…Four "work" stations were identified based on their proximity to the main business districts of Seoul and the relative absence of entertainment or residential areas: Gasan Digital Complex station, Yeoksam station, Yeouido station, and City Hall station. These stations have high numbers of alighting passengers in the morning and boarding passengers in the evening on weekdays, which is typical of workplace destinations [8].…”

Section: "Work" Stationsmentioning

confidence: 99%

“…Four "leisure" stations were identified based on the weekly pattern of passengers that peaked on weekends: Express Bus Terminal station, Yeongdeungpo station, Hyehwa station, and Itaewon station [8]. Express Bus Terminal station and Yeongdeungpo station are both major shopping destinations, each directly connected large shopping malls, while Hyehwa station and Itaewon station are famous for nearby venues for theatrical performances and nightlife, respectively.…”

Section: "Leisure" Stationsmentioning

confidence: 99%

“…The Seoul Metropolitan Subway is the most common form of public transportation used by the people in Seoul, with an average of 13 million boarding and alighting passengers per day. Some of the stations have specific age groups or specific diurnal and weekly patterns of passengers, so the changes in the number of passengers in these stations may provide valuable information regarding the changing behaviors of various population groups [8]. Thus, this study explores the changes in the number of subway passengers in Seoul at various time points of the COVID-19 outbreak in South Korea.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Changes in Subway Ridership in Response to COVID-19 in Seoul, South Korea: Implications for Social Distancing

Park¹

2020

Cureus

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Section: "Work" Stationsmentioning

confidence: 99%

Section: "Leisure" Stationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Changes in Subway Ridership in Response to COVID-19 in Seoul, South Korea: Implications for Social Distancing

Park¹

2020

Cureus

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“…This paper has addressed usage-based and location-based characterization of stops separately. Previous work (such as [17], [16], and [9]) has touched on the relationship between these types of features, by using one type for classification and examining the association between cluster membership and the other feature type. But it would be possible to combine both types of features as input in future work, along with additional usage-related information like absolute level and variance.…”

Section: Discussionmentioning

confidence: 99%

Mixed data and classification of transit stops

Tupper

Matteson

Handley

2016

2016 IEEE International Conference on Big Data (Big Data)

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Abstract-An analysis of the characteristics and behavior of individual bus stops can reveal clusters of similar stops, which can be of use in making routing and scheduling decisions, as well as determining what facilities to provide at each stop. This paper provides an exploratory analysis, including several possible clustering results, of a dataset provided by the Regional Transit Service of Rochester, NY. The dataset describes ridership on public buses, recording the time, location, and number of entering and exiting passengers each time a bus stops. A description of the overall behavior of bus ridership is followed by a stop-level analysis. We compare multiple measures of stop similarity, based on location, route information, and ridership volume over time.

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“…Wang et al [24] used the smart card management system to study the difference between directional traffic in different periods of time, and divided the typical stations in Hong Kong into three categories. Based on the smart card payment system, Kim et al [25] studied the passenger metro travel characteristics by K-means method, and the stations were divided into five types according to the land use properties around the metro. The results showed that there were obvious differences between the travel characteristics of Seoul metro stations and other related characteristics.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

Jing

et al. 2019

Sustainability

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The improvement of accuracy of short-term passenger flow prediction plays a key role in the efficient and sustainable development of metro operation. The primary objective of this study is to explore the factors that influence prediction accuracy from time granularity and station class. An important aim of the study was also in presenting the proposition of change in a forecasting method. Passenger flow data from 87 Metro stations in Xi’an was collected and analyzed. A framework of short-term passenger flow based on the Empirical Mode Decomposition-Support Vector Regression (EMD-SVR) was proposed to predict passenger flow for different types of stations. Also, the relationship between the generation of passenger flow prediction error and passenger flow data was investigated. First, the metro network was classified into four categories by using eight clustering factors based on the characteristics of inbound passenger flow. Second, Pearson correlation coefficient was utilized to explore the time interval and time granularity for short-term passenger flow prediction. Third, the EMD-SVR was used to predict the passenger flow in the optimal time interval for each station. Results showed that the proposed approach has a significant improvement compared to the traditional passenger flow forecast approach. Lookback Volatility (LVB) was applied to reflect the fluctuation difference of passenger flow data, and the linear fitting of prediction error was conducted. The goodness-of-fit (R2) was found to be 0.768, indicating a good fitting of the data. Furthermore, it revealed that there are obvious differences in the prediction error of the four kinds of stations.

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Ridership patterns at subway stations of Seoul capital area and characteristics of station influence area

Cited by 32 publications

References 11 publications

Changes in Subway Ridership in Response to COVID-19 in Seoul, South Korea: Implications for Social Distancing

Changes in Subway Ridership in Response to COVID-19 in Seoul, South Korea: Implications for Social Distancing

Mixed data and classification of transit stops

Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

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