• Proceedings of the KSR Conference
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• 2011.10a
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• pp.607-613
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• 2011

This paper reviews search methods of multiple reasonable paths to implement multimodal public transportation network of Seoul. Such a large scale multimodal public transportation network as Seoul, the computation time of path finding algorithm is a key and the result of path should reflect route choice behavior of public transportation passengers. Search method of alternative path is divided by removing path method and deviation path method. It analyzes previous researches based on the complexity of algorithm for large-scale network. Applying path finding algorithm in public transportation network, transfer and loop constraints must be included to be able to reflect real behavior. It constructs the generalized cost function based on the smart card data to reflect travel behavior of public transportation. To validate the availability of algorithm, experiments conducted with Seoul metropolitan public multimodal transportation network consisted with 22,109 nodes and 215,859 links by using the deviation path method, suitable for large-scale network.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.1-12
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• 2017

The Seoul metropolitan transportation card's high value lies in its recording of total population movements of the public transit system. In case of recorded information on transit by bus, even though route information utilized by each passenger is accurate, the lack of passenger transfer information of the urban railway makes it difficult to estimate correct routes taken by each passenger. Therefore, pinpointing passenger path selection patterns arising in the metropolitan railway network and using this as part of a path movement estimation model is essential. This research seeks to determine that features of passenger movement routes in the urban railway system is comprised of M-similar routes with increasing number of transfer reflected as additional costs. In order to construct the path finding conditions, an M-similar route searching method is proposed, embedded with non additive path cost which appears through inclusion of the stepwise transportation parameter. As well, sensitivity of the M-similar route method based on transportation card records is evaluated and a stochastic trip assignment model using M-similar path finding is constructed. From these, link trip and transfer trip results between lines of the Seoul metropolitan railway are presented.

• Journal of the Korean Society for Railway
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• v.19 no.5
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• pp.663-671
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• 2016

This paper investigates how to change passengers' preferences for express trains according to line extension in Seoul Metro Line 9. Before-and-after line extension, we traced passengers' path choices using Smart Card data via the method suggested by Kim et al. (2015). We developed two multinomial logit models before-and-after line extension. Transferability test showed that there is no difference between the two models. However, the load factor of the express train increased by 6.7% and the gap between the local and express trains became significantly wider.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.3D
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• pp.283-289
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• 2012

Because railways themselves are not enough to provide door-to-door service, they need to be complemented by a transportation system access the stations and a convenient transfer system to access other railway lines. User friendly service policies include a faster, interconnected, and intermodal transportation system. It becomes more important to use deep subterranean space from an economical standpoint in order to promote railway construction projects. In this research, the authors dealt with construction plan for strengthening capacity of deep subterranean railways. Since deep subterranean railways are situated deeper underground than other railways, they are more difficult to access and transfer to, in addition to psychological pressure. Moreover, deep subterranean railways with high speed add access difficulties as the distance between stations increases. Therefore, the authors discuss not only systemization for uniting deep subterranean railways and other transportation facilities but also reinforcement strategies. The purpose of these strategies is so that deep subterranean railways provide mobility while established railways provide accessibility to overcome this problem.

• 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.

• 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.36 no.1
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• pp.105-114
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• 2016

With approximately 20 million transportation card data entries of the metropolitan districts being generated per day, application of the data to management and policy interventions is becoming an issue of interest. The research herein attempts a model of the possibility of dynamic demand change predictions and its purpose is thereby to construct a Dynamic Passengers Trip Assignment Model. The model and algorithm created are targeted at city rail lines operated by seven different transport facilities with the exclusion of travel by bus, as passenger movements by this mode can be minutely disaggregated through card tagging. The model created has been constructed in continuous time as is fitting to the big data characteristic of transport card data, while passenger path choice behavior is effectively represented using a perception parameter as a function of increasing number of transfers. Running the model on 800 pairs of metropolitan city rail data has proven its capability in determining dynamic demand at any moment in time, in line with the typical advantages expected of a continuous time-based model. Comparison against data measured by the eye of existing rail operating facilities to assess changes in congestion intensity shows that the model closely approximates the values and trends of the existing data with high levels of confidence. Future research efforts should be directed toward continued examination into construction of an integrated bus-city rail system model.