• Journal of Korean Society of Transportation
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• v.35 no.5
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• pp.409-422
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• 2017

TOD (Transit-Oriented Development) is one of the urban structure concentrated on the multifunctional space/district with public transportation system, which is introduced for maintaining sustainable future cities. With such trends, the project of building complex transferring centers located at a urban railway station has widely been spreaded and a comprehensive and systematic analytical framework is required to clarify and readily understand the complicated procedure of estimation with the large scale of the project. By doing so, this study is to develop a comprehensive analytical framework for estimating a pedestrian OD matrix using a spatial information and an integrated smart card data, which is so called a data depository and it has been applied to the Samseong station for the model validation. The proposed analytical framework contributes on providing a chance to possibly extend with digitalized and automated data collection technologies and a BigData mining methods.

• Journal of the Korean Regional Science Association
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• v.33 no.1
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• pp.29-41
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• 2017

This study proposes an integrated accessibility measurement algorithm, which is applied to the Seoul Metropolitan public transportation system consisting of bus and subway networks, and analyzes the result. We construct a public transportation network graph linking bus-subway networks and take the time distance as the link weight in the graph. We develop a time-distance algorithm to measure the time distance between each pair of transit stations based on the T-card transaction database. The average travel time between nodes has been computed via the shortest-path algorithm applied to the time-distance matrix, which is obtained from the average speed of each transit route in the T-card transaction database. Here the walking time between nodes is also taken into account if walking is involved. The integrated time-distance accessibility of each node in the Seoul Metropolitan public transportation system has been computed from the T-card data of 2013. We make a comparison between the results and those of the bus system and of the subway system, and analyze the spatial patterns. This study is the first attempt to measure the integrated time-distance accessibility for the Seoul Metropolitan public transportation system consisting of 16,277 nodes with 600 bus routes and 16 subway lines.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.187-196
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• 2017

It is urgent to prepare countermeasures for traffic congestion problems of Korea's metropolitan area where central functions such as economic, social, cultural, and education are excessively concentrated. Most users of public transportation in metropolitan areas including Seoul use the traffic cards. If various information is extracted from traffic big data produced by the traffic cards, they can provide basic data for transport policies, land usages, or facility plans. Therefore, in this study, we extract valuable information such as the subway passengers' frequent travel patterns from the big traffic data provided by the Seoul Metropolitan Government Big Data Campus. For this, we use a Hadoop (High-Availability Distributed Object-Oriented Platform) to preprocess the big data and store it into a Mongo database in order to analyze it by a sequential pattern data mining technique. Since we analysis the actual big data, that is, the traffic cards' data provided by the Seoul Metropolitan Government Big Data Campus, the analyzed results can be used as an important referenced data when the Seoul government makes a plan about the metropolitan traffic policies.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.296-303
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• 2016

In public transportation, smart cards have been introduced for the purpose of convenient payment systems. The smart card transaction data can be utilized not only for the exact and convenient payment but also for civil planning based on travel tracking of citizens. This paper focuses on the analysis of the transportation convenience using the smart card big data. To this end, a new index is developed to measure the transit convenience of each region by considering how passengers actually experience the transportation network in their travels. The movement data such as movement distance, time and amount between regions are utilized to access the public transportation convenience of each region. A smart card data of five working days in March is used to evaluate the transit convenience of each region in Seoul city. The contribution of this study is that a new transit convenience measure was developed based on the reality data. It is expected that this measure can be used as a means of quantitative analysis in civil planning such as a traffic policy or local policy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.12-28
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• 2017

The core of the transportation service, so-called Mobility 4.0 is the flexibility of the entire mobility and its implementation. By doing so, the most essential element is to build a platform to link a supply and a demand simultaneously. In other word, a comprehensive analytical framework is to be set with a data repository which can be periodically updated. With such circumstances, the entire trip chain including pedestrian movements is required to be thoroughly investigated and constructed at the viewpoint of the intermodal transit station. A few studies, however, have been attempted. In this study, the comprehensive analytical framework with the integrated mobility at intermodal transit station was proposed, which consisted of the three modules; 1) Data Repository Extracting from Smart Card DB, 2) Framework of Analyzing Integrated Mobility, and 3) Interpretation of the Integrated Mobility with GIS information and the other factors. A case study with the seven railway stations (Sadang, Sindorom, Samseong, Gwanghwanoon, Gangnam, Jamsil, Seoul Nat'l Univ. of Education) was conducted. The stations of the case study were clustered by the three groups with the statistical ground, and it is most likely to understand the effect of a variety of factors and a comprehensive data-driven analyses with the entire trip stages.