• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.80-93
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• 2018

Public transit services are generally analyzed based on the correlation of demand and supply. The computation of supply uses accessibility while demand uses travel demands estimation based on residential population. However, the traditional demand estimation has a limitation in analysing in micro-scale compared to the smartcard data traffic. This study analyzed potential improvement of public transit services using smartcard traffic data. The supply of transportation was defined using time distance accessibility. Also, time loss was calculated in those origin destination(OD) pairs where time distance accessibilities are relatively low. The proposed method was applied at Seoul. The results showed that the areas where OD pairs need improvement include Seodaemun-gu, Guro-gu and Nowon-gu.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.109-119
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• 2008

The number of card users has grown steadily after the adaption of smart card. Considering the diverse information from smart card data, the increase of card usage rate leads to various useful implications meaning in travel pattern analysis and transportation policy. One of the most important implications is the possibility that the data enables us to generate transit O/D tables easily. In the case of generating transit O/D tables from smart card data, it is necessary to filter data error and/or data missing. Also, the correction of data missing is an important procedure. In this study, it is examined to compute the level of data error and data missing, and to correct data missing for transit O/D generation.

• Proceedings of the KOR-KST Conference
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• 2002.02a
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• pp.45-77
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• 2002

교통혼잡 문제가 점점 심각해짐에 따라 대중교통의 중요성은 날로 부각되며, 대중교통을 지원하기 위한 정책들이 속속 입안되고 있어 대중교통을 심도 있게 분석할 수 있는 틀의 개발은 필연적이라 할 수 있다. 이에 본 연구는 대중교통 통행배정모형 개발과 대중교통 기종점통행량(OD) 추정을 목적으로 수행되었다. 대중교통 통행배정모형의 개발부분에서는 기존의 대중교통 통행배정모형이 개별차량과 다른 대중교통의 특성을 정확히 반영하고 있지 못하다는 한계를 극복하고자, 최적경로 탐색에는 유전자 알고리즘(Genetic Algorithm)을 통행량 배정에는 로짓모형을 기반으로 한 확률적 통행량 배정모형(Stochastic Network Loading Model)을 이용하여 TATSN 모형을 개발하였다. 그리고, 대중교통 기종점통행량의 추정은 전통적인 기종점통행량 추정 방법인 기종점조사 방법이 시간과 비용이 과대하게 소요된다는 단점을 인식하여 관측통행량을 이용하여 추정하는 방법을 제안하였다.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1799-1810
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• 2011

Origin-Destination(OD) trip survey being used in travel demand forecasting has been obtained through totalizing process with direct sample survey techniques such as plate license survey, roadside interview, household travel survey, and cordon line counts. However, the OD survey has many discrepancies in sampling, totalizing process, and such discrepancies contains problems of difference between forecasted traffic volume and observed data. On the other hand, transit smart card data recently collected has credible resource of obtaining travel information for bus and metro. This paper presents parameter estimation of gravity model by using transit smart card data. Through the parameter estimation method, we estimated =0.57, ${\beta}$=0.14 of gravity model for bus, and ${\alpha}$=-0.21, ${\beta}$=0.05 for metro. The statistical test such as T-test, coefficient of correlation, Theil`s inequality coefficient showed no difference between observed volume and estimated volume. Elasticities of bus and metro derived in this paper are also reasonable.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.11-30
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• 2019

Smartcards are used as the basic data for utilizing the various transportation policies and evaluations, etc. and provided the transportation basic statistics index. However, the main problem of the smartcard data is that the most of users do not take the alighting tag at the stop, so there is a limit to the scope of use for the total O-D trip data because incomplete O-D traffic data of transportation card users. In this study, a decision tree of learning model is estimated for the alighting stop of smartcard users. The model estimation accuracy in range less than 2 stops interval was 89.7% on average. By eliminating the incompleteness alighting stop of smartcard data through this model, it is expected to be used as the basic data for various transportation analyses and evaluations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.219-232
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• 2023

Mobility services need to change according to the regional characteristics of the target service area. Accordingly, analysis of mobility patterns and characteristics based on Origin-Destination (OD) data that reflect travel behaviors in the target service area is required. However, since conventional methods construct the OD data obtained from the administrative district-based zone system, it is hard to ensure spatial homogeneity. Hence, there are limitations in analyzing the inherent travel patterns of each mobility service, particularly for new mobility service like Demand Responsive Transit (DRT). Unlike the conventional approach, this study applies a data-driven clustering technique to conduct spatial analyses on OD travel patterns of regional mobility services based on reconstructed OD data derived from re-aggregation for original OD distributions. Based on the reconstructed OD data that contains information on the inherent feature vectors of the original OD data, the proposed method enables analysis of the spatial characteristics of regional mobility services, including public transit bus, taxi and DRT.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.5
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• pp.12-19
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• 2016

An average of 20 million individual transit unit activities per day on the Seoul-Gyeonggi-Incheon public transportation network are provided as transportation card analysis data by the metropolitan district (99.02% by 2014 standard, Humanlive, 2015.4). The metropolitan transportation card data can be employed in a comprehensive analysis of public transportation users' current transit patterns and by means of this, an effective use plan can be explored. In enhancing the existing information on the bus and rail integrated network of the metropolis with public transportation card data, the constraints in the existing methodology of metropolitan transit analysis, which functions on a zone unit origin and destination basis, can be overcome. Framework for metropolitan public transportation card data based integrated public transportation analysis, which consists of bus and rail integrated transport modes, is constructed, and through this, a single passenger's transit behavior transit volume can be approximated. This research proposes that in the use of metropolitan public transportation card data, integrated public transportation usage, as a part of individual passenger spatial movements, can be analyzed. Furthermore, metropolitan public transportation card usage data can provide insights into understanding not only movements of populations taking on transit activities, but also, characteristics of metropolitan local space.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.165-179
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• 2005

The object of this study is a development of a stochastic user equilibrium transit assignment algorithm for multiple user classes considering stochastic characteristics and heterogeneous attributes of passengers. The existing transit assignment algorithms have limits to attain realistic results because they assume a characteristic of passengers to be equal. Although one group with transit information and the other group without it have different trip patterns, the past studies could not explain the differences. For overcoming the problems, we use following methods. First, we apply a stochastic transit assignment model to obtain the difference of the perceived travel cost between passengers and apply a multiple user class assignment model to obtain the heterogeneous qualify of groups to get realistic results. Second, we assume that person trips have influence on the travel cost function in the development of model. Third, we use a C-logit model for solving IIA(independence of irrelevant alternatives) problems. According to repetition assigned trips and equivalent path cost have difference by each group and each path. The result comes close to stochastic user equilibrium and converging speed is very fast. The algorithm of this study is expected to make good use of evaluation tools in the transit policies by applying heterogeneous attributes and OD data.

• 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 Society for Railway
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• v.20 no.5
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• pp.692-702
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• 2017

Recently, there has been a growing interest in using smart card data. However, there are restrictions on the utilization of data in many areas outside the Seoul metropolitan area because the data does not contain alighting information. This paper presents a methodology for estimating alighting stops of smart card data. Estimation results were verified by smart card data from Seoul and Gwangju. The estimation rates were 78.2% and 81.6% in Seoul and Gwangju, respectively. The matching accuracy was 54.2% and 33.4%, respectively. However, if up to two stops of error are allowed, the accuracy values were 93.6% and 94.0%, respectively. We also discussed changes in estimation results due to adjusting the allowable walking distance, which is a key parameter of trip chaining methods. As the allowable walking distance increases, the estimation rate increases, while the accuracy decreases, and it is found that the estimation results change by around 500m.