• Journal of the Economic Geographical Society of Korea
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• v.9 no.3
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• pp.379-395
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• 2006

The purpose of this study is to analyze the spatial characteristics of travel patterns and travel behaviors of transit users in the Metropolitan Seoul area. We apply the data mining techniques to explore the travel patterns of transit users from the T-money card database which has been produced over 10,000,000 transaction records per day. The database contains the information of locations and times of origin, transfer, and destination points for each transaction as well as the informations of transit modes taken via the transaction. We develop an data mining algorithm to explore traversal patterns from the enormous information. The algorithm determines the travel sequences of each passenger, and produce the volumes of support on each points (stops) of transportation networks in the Metropolitan Seoul area. In order to visualize the spatial patterns of travel demands for transit systems we apply GIS techniques, and attempt to investigate the spatial characteristics of travel patterns and travel demand. Subway stops located in the Gangnam area appear the highest peak for the travel origin and destination, while the CBD in the Gangbuk stands at the second position. Two or three sub-peaks appear at the densely populated residential areas developed as the high-rise apartment complex. Subway stations located along the Subway Line 2, especially from Guro to Samsung receive heavy travel demand (total support), while bus stops located at the CBD in the Gangbuk stands the highest travel demand by bus.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.87-97
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• 2008

The traditional trip-based modeling approach has assumed that a trip is generated for a purpose. However, the approach has not considered trips as a set of connected trips nor has it considered trip chaining. The purpose of this study is to identify general characteristics of trip chaining, and to explore relationships between trip purposes and trip chains using multivariate regression models. The data for this study come from the 2006 Seoul household travel diary survey. It is found that simple trip chains are dominant phenomena, and socio-economic characteristics such as occupation, income, age, and gender are closely related to types of trip chains. People aged less than 20, females, or high-income people are more likely to have a higher number of home-based trip chains. In addition, commute and school trips for workers and students respectively tend to be strongly associated with simple trip chains, while shopping and leisure trips for housewives tend to be related to simple trip chains.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.67-83
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• 2018

This research is motivated by the question, "Where, when, and through what mode does an individual passenger moving within a subway station use to travel from starting to final destinations ?" To answer this, the stations passed by the individual passenger, the path taken, and modes used need to be known beforehand. In the metropolitan integrated public transportation fare system, Automated Fare Collection System(AFC) can be a source of information on transit modes, stations, and paths of individual passengers. AFC calculates a fare for the passenger based on travel data such as boarding and alighting stations, time, and mode used. In this research, an Selected Station Analysis(SSA) method, in which AFC data is used to observe passenger movement in the metropolitan public transportation subway station from the perspective of subway transfer stations, is proposed. SSA subdivides individual passenger movement in transfer stations and analyzes initial station/time and final destination station/time information using the trip chain perspective.

• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.129-139
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• 2010

This study proposes a model for measuring the connectivity of nodes in road networks. The connectivity index between two nodes is characterized by the number of routes, degree of circuitousness, design speed, and route capacity between the nodes. The connectivity index of a node is then defined as the weighted average of the connectivity indexes between the node and other nodes under consideration. The weighting factor between two nodes is determined by the travel demand and distance between them. The application of the model to a toy network shows that it reasonably well quantifies the level of connectivity of nodes in the network. If flow of rail networks can be measured in the same scale as that of road networks and the capacity of rail links can be estimated, the model proposed in this paper could be applied to intermodal transportation networks as well.

• Proceedings of the KOR-KST Conference
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• 1999.03a
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• pp.77-92
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• 1999

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.29-41
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• 2011

This study investigates the factor that influences commuting time to work when individuals allocate their time for different types of activities. The commuting time is an important indicator for an individual to determine the residence and choose the means of transportation. The analysis uses the data collected from people who live in Seoul metropolitan area including Seoul, Incheon and Gyeonggi Province, and commute to work and making the simple linkage travel (home-work-home) within the area. For the analysis, the Cox hazard proportional methodology was adopted. The method is known to be well applied without assuming any distribution in case of the dependent variable being continuous. For the covariate, the interaction effect between the space variable of the work place and the variable of transportation has been also included in the model. The commuting time to work has been estimated for both 1) the whole metropolitan area and 2) the separate regions i.e., Seoul, Incheon and Gyeonggi-Do. The result reveals that characteristic variables related to individual, household and travel properties influence the mode of transportation and the time allocated for commuting to work (p<0.01). This study also demonstrates the usefulness of the Cox hazard proportional model. The data used in this study is the actual household travel data surveyed in 2006 in the metropolitan area, and analyzing the survey data in 2010 is currently in progress. Comparison of the two survey data sets seeking any behavioral change is suggested for the future study.

• Journal of the Korean Society for Railway
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• v.14 no.4
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• pp.383-389
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• 2011

Until now, in planning and constructing KTX and the Express Way, the connectivity and transfer between these facilities have not been considered. In this study the effect of mode choice behavior by connecting KTX and the Express Way is analyzed through estimating Multinomial Logit Model and Binary Logit Model. The SP and RP surveys to develop these models were carried out and the data were selected from the passengers using the KTX station, Express Bus Terminals and Rest Areas in the Express Way. To test the effect of connectivity and transfer in the field, the case study for Dongtan KTX station was carried out. According to the results, connecting the KTX station and the Express Way has the effect of increasing the demand by 30%. And this is caused by saving about 120 minutes of traveling time from Seoul to Pusan. This study shows that the connectivity and transfer can increase the efficiency of transportation system and the improvement in the mobility and accessibility will maximize the usages of these two facilities.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.04a
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• pp.33-35
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• 2000

대도시 대기오염을 정확하게 평가하기 위해서는 자동차에 의한 대기오염물질 배출량이 정확하게 산출되어야 한다. 그러나 현재 우리 나라는 이동오염원에 의한 배출량 산출자료가 부족한 상태이다. 그 이유는 배출량 산출방법이 정립되어 있지 못하고 관련된 기초자료가 다양하며, 연계가 잘 이루어지지 못하였기 때문이다. 본 연구에서는 이동오염원중 자동차에 의한 지역별 대기오염 배출량 산출을 개선하기 위하여 기존의 지역별 둥록대수에 의한 방법 대신 지역별 통행량을 고려한 자동차 부문 대기오염배출량 산출 방법을 비교 검토하고자 한다.(중략)

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.50-63
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• 2017

While transit is inconvenient, it is also inevitable for the efficient public transportation. Reducing the number of transfers as much as possible is most important in providing the convenience of public transportation and facilitating the public transportation. As for the public transportation card data, 61,986 items on weekdays and 69,100 items on weekends were collected. Pattern analysis and traffic influence factors were analyzed using traffic data card. Trip chain results revealed that people have more transit transfers for shopping and leasure than commuting purposes on weekends and that commuting distance and time increase by 10 km and 9.9 minutes, respectively. Besides, results of the structural equation model showed that factor 1(total travel time, total travel distance), factor 2(number of people getting on and off), factor 3(transit time), and factor 4(number of bus connections, number of operations) were found to have significant effects on the number of transfers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2D
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• pp.185-192
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• 2011

More practical outputs and insights can be obtained through transportation analysis considering the time-dependent traffic movements. This study proposes a method of constructing time-dependent O-D trip tables for expressway corridor using real-world individual trip data. In this study, time-dependent O-D trip tables for the nationwide highway network are constructed based on toll collection system data. The proposed methodology is to convert nationwide time-dependent O-D trip tables into Korean expressway corridor O-D trip tables in order to deal with the computational complexity arising from simulating a large-scale traffic network. The experiment results suggest that actual individual trip record data can be used to effectively construct time-dependent O-D trip tables. They also imply that the construction of time-dependent O-D trip tables for the national highway networks along with those for Korean expressway developed in this study would make transportation analysis more practical and applicable to real-time traffic operation and control.