• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.18-25
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• 2019

The public data portal provides various public data created by the government in the form of files and open APIs. In order to increase the usability of public open data, a variety of information should be provided to users and should be convenient to use for users. This requires the structured data design plan of the public data. In this paper, we propose a data design method to improve the usability of the Seoul subway public data. For the study, we first identify some properties of the current subway public data and then classify the data based on these properties. The properties used as classification criteria are stored properties, derived properties, static properties, and dynamic properties. We also analyze the limitations of current data for each property. Based on this analysis, we classify currently used subway public data into code entities, base entities, and history entities and present the improved design of entities according to this classification. In addition, we propose data retrieval functions to increase the utilization of the data. If the data is designed according to the proposed design of this paper, it will be possible to solve the problem of duplication and inconsistency of the data currently used and to implement more structural data. As a result, it can provide more functions for users, which is the basis for increasing usability of subway public data.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.51-58
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• 2018

In order to utilize various data provided by Korea public open data portal, data should be systematically managed using a database. Since the range of open data is enormous, and the amount of data continues to increase, it is preferable to use a database capable of processing big data in order to analyze and utilize the data. This paper proposes data modeling and implementation method suitable for public data. The target data is subway related data provided by the public open data portal. Schema of the public data related to Seoul metro stations are analyzed and problems of the schema are presented. To solve these problems, this paper proposes a method to normalize and structure the subway data and model it in NoSQL database. In addition, the implementation result is shown by using MongDB which is a document-based database capable of processing big data.

• Journal of Preventive Medicine and Public Health
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• v.39 no.4
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• pp.325-330
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• 2006

Objectives: The aims of this study were to examine the level of airborne fungi and environmental factors in Seoul metropolitan subway stations and to provide fundamental data to protect the health of subway workers and passengers. Methods: The field survey was performed from November in 2004 to February in 2005. A total 22 subway stations located at Seoul subway lines 1-4 were randomly selected. The measurement points were subway workers' activity areas (station office, bedroom, ticket office and driver's seat) and the passengers' activity areas (station precincts, inside train and platform). Air sampling for collecting airborne fungi was carried out using a one-stage cascade impactor. The PM and CO2 were measured using an electronic direct recorder and detecting tube, respectively. Results: In the activity areas of the subway workers and passengers, the mean concentrations of airborne fungi were relatively higher in the workers' bedroom and station precinct whereas the concentration of particulate matter, $PM_{10}\;and\;PM_{2.5}$, were relatively higher in the platform, inside the train and driver's seat than in the other activity areas. There was no significant difference in the concentration of airborne fungi between the underground and ground activity areas of the subway. The mean $PM_{10}\;and\;PM_{2.5}$ concentration in the platform located at underground was significantly higher than that of the ground (p<0.05). Conclusions: The levels of airborne fungi in the Seoul subway line 1-4 were not serious enough to cause respiratory disease in subway workers and passengers. This indicates that there is little correlation between airborne fungi and particulate matter.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.125-138
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• 2020

Currently, subway crowding is estimated by observing a specific point at specific hours once or twice every 1 or 2 years. Given the extensive subway network in Seoul Metropolitan Area covering 588 stations, 11 lines and 80 transfer stations as of 2017, implementing crowding mitigation policy may have its limitations due to data uncertainty. A proposal has recently been made to effectively use smart card data, which generates big data on the overall subway traffic related to an estimated 8 million passengers per day. To mitigate subway crowding, this study proposes two viable options based on data related to smart card used in Seoul Metropolitan Area. One is to create a subway passenger pattern model to accurately estimate subway crowding, while the other is to prove effectiveness of early bird policy to distribute subway demand that is concentrated at certain stations and certain time. A subway passenger pattern model was created to estimate the passenger routes based on subway terminal ID at the entrance and exit and data by hours. To that end, we propose assigning passengers at the routes similar to the shortest routes based on an assumption that passengers choose the fastest routes. In the model, passenger flow is simulated every minute, and subway crowding level by station and line at every hour is analyzed while station usage pattern is identified by depending on passenger paths. For early bird policy, highly crowded stations will be categorized based on congestion level extracted from subway passenger pattern model and viability of a policy which transfers certain traveling demands to early commuting hours in those stations will be reviewed. In particular, review will be conducted on the impact of policy implemented at certain stations on other stations and lines from subway network as a whole. Lastly, we proposed that smart card based subway passenger pattern model established through this study used in decision making process to ensure effective implementation of public transport policy.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.7
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• pp.652-657
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• 2004

Development of underground space in urban area has a huge amount of potential to ease the limitations on the land use and the efficiency especially in urban area. Considering public transportation in urban area, subway system could be one of the most efficient and practical approaches. Subsequently this leads the public to have more chances to experience the indoor air quality (IAQ) in subway systems. In this study, it was aimed to produce useful data for the IAQ control in subway environments, Specifically the train-induced wind has been investigated by means of field survey and analysis. The recent updates including the quantified characteristics of train-induced wind are presented in this paper.

• Journal of the Korean Society for Railway
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• v.18 no.3
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• pp.270-277
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• 2015

In some sections of the Seoul Metropolitan Subway, severe congestion can be observed during rush hours and on specific days. The subway operators have been conducting regular surveys to measure the level of congestion on trains: the results are then used to make plans for congestion reduction. However, the survey has so far focused just on train' congestion and has been unable to determine non-recurring congestion due to special events. This study develops an algorithm to estimate the platform congestion rate by time using individual public transportation card data. The algorithm is evaluated by comparison of the estimated congestion rate and the ground truth data that are actually observed at non-transfer subway stations on Seoul subway line 2. The error rates are within ${pm}2%$ and the performance of the algorithm is fairly good. However, varying walking times from gates to platforms, which are applied to both non-peak periods and peak time periods, are needed to improve the algorithm.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.145-150
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• 2023

The subway is a public transportation that many people use every day. Line 2 especially has the most crowded stations during the day. However, the risk of crush accidents is increasing due to high congestion during rush hour and this reduces the safety and comfort of passengers. Subway congestion prediction is helpful to forestall problems caused by high congestion. Therefore, this study proposes machine learning classification models that predict subway congestion during commuting time. To predict congestion in Line 2 based in machine learning, we investigate variables that affect subway congestion through previous research and collect a dataset of subway congestion on Line 2 during rush hour from PUBLIC DATA PORTAL. The proposed model is expected to establish the subway operation plane to make passengers safe and satisfied.

• Spatial Information Research
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• v.19 no.4
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• pp.11-20
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• 2011

In accordance with low carbon and green growth paradigm, a subway is one of major public transit systems for resolving traffic congestion and decreasing traffic accidents. In addition, as subway networks expand, passengers' travel pattern in the subway network change and consequently affect the urban structure. Generally, new subway route has been planned and developed, mainly considering a travel demand forecast. However, it is desired to conduct an empirical analysis on the forecast model regarding change of travel accessibility and passenger demand pattern according to new subway line. Therefore, in this paper, an alternative method, developed based upon a spatial syntax model, is proposed for evaluating new subway route in terms of passenger's mobility and network accessibility. In a case study, we constructed subway network data, mainly targeting the no 9 subway line opened in 2009. With an axial-map analysis, we calculated spatial characteristics to describe topological movement interface. We then analyzed actual modal shift and change on demand of passengers through the number of subway passenger between subway stations and the number of passenger according to comparative bus line from Smart Card to validate suggested methods. Results show that the proposed method provides quantitative means of visualizing passenger flow in subway route planning and of analyzing the time-space characteristics of network. Also, it is expected that the proposed method can be utilized for predicting a passengers' pattern and its impact on public transportation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.2
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• pp.107-115
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• 2007

Among public transportation facilities within urban area, electric railway (subway) has been a regionally based facility that has played an important role in improving the foundation of territory development and arrangement of living foundation and living environment while supplementing the regional road network. In this regard, the subway stations should be allocated in the right place to ensure mobility, convenience and economic feasibility, some of transportation characteristics of road network combined with the subway. However, it would be very hard to evaluate quantitatively the effects of public transportation facilities such as subway in metropolitan cities on regional development and change in land use and to suggest the data that would be utilized in future city planning corresponding to their results. Therefore, this study evaluated the change in land use by the conditions of location of subway stations quantitatively; then, it evaluated and analyzed the change in land use for the internal and external parts of the surrounding areas of subway stations through the GIS spatial analysis and classification of landsat TM satellite image for utilizing it as reference material for the new establishment of subway stations in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.13-18
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• 2018

In this study, we classify Seoul metro stations according to boarding and alighting patterns using machine earning technique. The target data is the number of boarding and alighting passengers per hour every day at 233 subway stations from 2008 to 2017 provided by the public data portal. Gaussian mixture model (GMM) and K-means clustering are used as machine learning techniques in order to classify subway stations. The distribution of the boarding time and the alighting time of the passengers can be modeled by the Gaussian mixture model. K-means clustering algorithm is used for unsupervised learning based on the data obtained by GMM modeling. As a result of the research, Seoul metro stations are classified into four groups according to boarding and alighting patterns. The results of this study can be utilized as a basic knowledge for analyzing the characteristics of Seoul subway stations and analyzing it economically, socially and culturally. The method of this research can be applied to public data and big data in areas requiring clustering.