• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.33-43
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• 2016

Public transportation card data, which is collected for purposes of the Integrated Public Transportation Fare System, provides neither transfer time nor transfer frequency occurring on the metropolitan city-rail (MCR). And because there are no transfer toll gates installed on the MCR, data on transfers between lines are estimated through means such as elicitations using survey questionnaire, or otherwise through macroscopic observations, which poses the risk of transfer time and frequencies being underestimated. For the accurate estimation thereof, an explanation of the transit path that arises between the Entry-and Exit-Gates must be provided. The purpose of this research is twofold : 1) to build a transit path model to reflect the current state of transfer movements on the basis of transportation card reader data, and 2) to deduce information on transfers occurring in the greater metropolis. To achieve these aims, the idea of Big Nodes is introduced in the model to align transportation card reader operation system characteristics with those of the MCR network. The link-label method is applied in the model as well to make certain that the MCR network runs in an effective manner. Administrative information obtained by the transportation card reader is used to derive transfer time and frequency both in the city's mid-zones, and in the Seoul-Gyeonggi-Incheon district's large-zones. Public transportation card data from a single specific day in year 2014 is employed in the building of the quantified transfer specific data. Extended usage thereof as providing comprehensive data of transfer resistance on the MCR is also examined.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.2
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• pp.1-12
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• 2016

This paper is to analyze characteristics of transfer trips and to identify impacts of land use on them. Using the smart transport card data of Seoul on a weekday in April 2013, we explored general characteristics of the transfer trips such as spatial and temporal distributions, transfer types, and geographical patterns of transfer trips. Then, the multiple regression model for the transfer trips was developed, considering land use as well as socio-economic variables as explanatory ones. For the characteristics of the transfer trips, their ratio to the total trips accounts for 26.7%. Nearly 87% of the trips are one-time transferred, and 64.7% are bus-subway transfer trips. In addition, the transfer trips are more likely to appear nearby subway stations and business facilities. The regression model indicates that land use variables such as the floor areas of business facilities and department stores and mixed land use index significantly positively affect the transfer trips. Our results can be used as basic data for choosing feasible locations of multi-modal transfer centers in urban areas.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.6
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• pp.1083-1091
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• 2016

This research uses public transportation card data to analyze the inter-regional transfer times, transfer frequencies, and transfer resistance that passengers experience during transit amongst the metropolitan public transportation modes. Currently, mode transfers between bus and rail are recorded up to five times during one transit movement by Trip Chain, facilitating greater comprehension of intermodal movements. However, lack of information on what arises during these transfers poses a problem in that it leads to an underestimation of transfer resistances on the Trip Chain. As such, a path choice model that reflects passenger movements during transit activities is created, which attains explanatory power on transfer resistance through its inclusion of transfer times and frequencies. The methodology adopted in this research is to first conceptualize the idea of metropolitan public transportation transfer, and in the case that mode transfers include the city-rail, to newly conceptualize the idea of transfer resistance using transportation card data. Also, the city-rail path choice model within the Trip Chain is constructed, with transfer time and frequency used to reevaluate transfer resistance. Further, in order to align bus and city-rail station administrative level small-zone coordinates to state and regional level mid-zone coordinates, the big node methdod is utilized. Finally, case studies on trip chains using at least one transfer onto the city-rail is used to determine the validity of the results obtained.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.613-621
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• 2019

In general, transfer in subway stations is defined as transfer between lines and station transfer. In transfer between lines, passengers change from one subway line to another by utilizing horizontal pedestrian facilities such as transfer passages and pedestrian way. Station transfer appears in the situation that subway lines of enter and exit gate terminals differs from those of boarding and alighting trains and passenger trips utilize both vertical pedestrian facilities such as stair and escalator and horizontal facilities. The hypothesis on these two transfers presupposes that all subway lines are operated by either local train or express in subway network. This means that in a transfer case both local and express trains are operated in the same subway line, as a case of Seoul Metro Line 9, has not been studied. This research proposes a methodology of finding the same line transfer in the Seoul metropolitan subway network built based on the smart card network data by suggesting expanded network concept and a model that passengers choose a theirs minimum time routes.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.19-30
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• 2000

The purpose of the Paper is to estimate values of transfer related variables on route choices of subway users in Seoul. Four attributes were estimated affecting route choices, i.e. in-vehicle time, transfer time, number of transfers and existence of escalators. Stated preference and revealed preference techniques were used to estimate these values. The values of transfer related variables can be shown by in-vehicle time equivalents. One minute of transfer is 1.5∼2 times one minute of in vehicle times one time of transfer is 10∼15 times, and the existence of escalator is 2∼8 times. Women generally show stronger impedance than men and the elder does than the younger. Working, commuting and educational trips show stronger impedance than others too.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.14-24
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• 2018

Pedestrian movement of passengers using smart card within stations can be divided into three types of activities - straight ride and alight, line transfer, and station transfer. Straight ride and alight is transfer activity for which the card terminal and embarking line are identical. In this case, straight ride occurs at the origin station and straight alight occurs at the destination station. Line transfer refers to activity in which the subway line embarked on by the passenger is different from that which is disembarked. Succinctly, line transfer is transfer at a middle station, rather than at origin or destination stations. Station transfer occurs when the card terminal line and embarking line are different. It appears when station transfer happens at the origin station as starting transfer, and at the destination station as destination transfer. In the case of Metropolitan smart card data, origin and destination station card terminal line number data is recorded, but subway line data does not exist. Consequently, transportation card data, as it exists, cannot adequately be used to analyze pedestrian movement as a whole in subway stations. This research uses the smart card data, with its constraints, to propose an analysis model for passenger pedestrian movement within subway stations. To achieve this, a path selection model is constructed, which links origin and destination stations, and then applied for analysis. Finally, a case study of the metropolitan subway is undertaken and pedestrian volume analyzed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.539-547
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• 2012

Increasing the mode transfer volume between public transportation modes has known to be necessary for efficiency improvement of public transportation system operation and it is also found to be important to have relevant transfer point selection with reflection of current travel pattern. This study is in regards to providing a selection guideline for the location of transfer point between public transport modes. This case study has been carried out for Daegu Metropolitan City especially for public transportation users behaviour by analysis of daily usage of transportation card to identify the transfer travel pattern. A cluster analysis was applied to categorize the pattern of transfer stop which induces many users and a discriminant analysis also utilized for grouping the stops by number of transfer trip. This research produces the estimation result of transfer volume for urban railway system no.3 in Daegu City which is currently under construction. In addition, the locations of transfer center has also been proposed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.4D
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• pp.519-526
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• 2011

In this study, the characteristics of transfer station are analyzed using the data of public transportation. The traffic card of Daegu does not include boarding information. The boarding data are calculated using traffic card data and the BMS data. It is found that transfer has increased by the distance from CBD and the numbers of routes, and decresed by the waiting time. Oneway ANNOVA are carried out to find the optimal number of clusters. Three clusters are chosen in this study. The center of the first cluster shows 2.99, so it has a characteristic of CBD. The second is 6.73, the outskirts of town, and the third is 12.78, the outlying areas.

• 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.38 no.5
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• pp.693-701
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• 2018

Transfer types at the Seoul Metropolitan Urban Railway Stations can be classified into transfer between lines and station transfer. Station transfer is defined as occurring when either 1) the operating line that operates the tag-in card-reader and that operating the first train boarded by the passenger are different; or 2) the line operating the final alighted train and that operating the tag-out card-reader are different. In existing research, transportation card data is used to estimate transfer volume between lines, but excludes station transfer volume which leads to underestimation of volume through transfer passages. This research applies transportation card data to a method for station transfer volume estimation. To achieve this, the passenger path choice model is made appropriate for station transfer estimation using a modified big-node based network construction and data structure method. Case study analysis is performed using about 8 million daily data inputs from the metropolitan urban railway.