• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.58-71
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• 2021

The number of elderly people worldwide is rapidly increasing and the mobility handicapped suffering from inconvenient public transportation service is also increasing. In Korea and abroad, various policies are being implemented to provide high-quality transportation services for the mobility handicapped, and budget support and investment related to mobility facilities are being expanded. The mobility handicapped spends more time for transit transfer than normal users and their satisfaction with transit service is also lower. There exist transfer inconvenience points of the mobility handicapped due to various factors such as long transfer distances, absence of transportation facilities like elevators, escalators, etc. The purpose of this study is to find transfer inconvenience points for convenient transit transfer of the mobility handicapped using Smart card Big data. This study process traffic card transaction data and construct transfer travel data by user groups using smart card big data and analysis of the transfer characteristics for each user group ; normal, children, elderly, etc. Finally, find transfer inconveniences points by comparing transfer patterns between normal users and the mobility handicapped. This study is significant in that it can find transfer inconvenience points for convenient transit transfer of the mobility handicapped using Smart card Big data. In addition, it can be applicated of Smart card Big data for developing public transportation polices in the future. It is expected that the result of this study be used to improve the accessibility of transit transportation for mobility handicapped.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.615-627
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• 2012

This study analyzed the travel pattern with respect to use of public transportation by using transportation card data and presented the measures that can be used in a traffic policy. Transportation card data targeted Gyeonggi-Do area and as a utilization plan, a scenario that when a traffic policy decision maker improves bus stop facilities, the person selects a target site by using several variables that can be obtained from transportation card data was set and analyzed. The analysis result showed that K means cluster analysis which is decision making methodology and CHAID(Chi-squared automatic interaction detection) were used and it can be used usefully in policies in significance level of p <0.01. Also, based on these results, this study presented policy implications to be improved to actually use transportation card data in policies.

• Transportation Technology and Policy
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• v.9 no.6
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• pp.36-42
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• 2012

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.731-740
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• 2007

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.131-146
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• 2023

Smart card data is widely used in the public transportation field. Despite the inevitability of various errors occur during the data collection and storage; however, smart card data errors have not been extensively studied. This paper investigates inherent errors in boarding and alighting station information in smart card data. A comparison smart card data and bus boarding and alighting survey data for the same time frame shows that boarding station names differ by 6.2% between the two data sets. This indicates that the error rate of smart card data is 6.2% in terms of boarding station information, given that bus boarding and alighting survey data can be considered as ground truth. This paper propose 6-step algorithm for correcting errors in smart card boarding station information, linking them to corresponding information in Bus Information System(BIS) Data. Comparing BIS data and bus boarding and alighting survey data for the same time frame reveals that boarding station names correspond by 98.3% between the two data sets, indicating that BIS data can be used as reliable reference for ground truth. To evaluate its performance, applying the 6-step algorithm proposed in this paper to smart card data set shows that the error rate of boarding station information is reduced from 6.2% to 1.0%, resulting in a 5.2%p improvement in the accuracy of smart card data. It is expected that the proposed algorithm will enhance the process of adjusting bus routes and making decisions related to public transportation infrastructure investments.

• 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.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.231-245
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• 2020

The purpose of this study was to analyze the spatiotemporal characteristics of staying places by deriving the main staying places of the elderly population in Seoul using public transportation card data. For this reason, we used public transportation card data for 7 days from October 12, 2015 to October 18, 2015. As a result of the analysis, 14 places were extracted as the main staying places. It was divided into 5 groups based on the characteristics of the visiting users and concentration time. Most of the staying places showed that the elderly users who lived near the places visited, but in the case of the group where a large number of elderly users visited had relatively wide range of residential distribution. It was possible to confirm that there was a hierarchy. And the concentration time was displayed differently. Most of the staying places' concentration time was between 10 am and 5 pm. However, in the case of Jegi-dong group and Jamsil group had different concentration time. The results of this research provide necessary suggestions for establishing a public transport policy that considers the main stay spaces of the elderly population in Seoul and the stay characteristics of each stay space.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.58-72
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• 2018

Ganghwa has met a new development period in land use and infrastructure based on the 4th National Development Planning, however the public transportation system is not systematically operated yet. This paper analyzes the bus trip pattern in Ganghwa using transportation card data during a week. The result indicates that average 7,100 people use buses a day and the most frequent use occurred in Friday. Clear peak-hours between 7 and 8 A.M. and between 4 and 5 P.M. were appeared due to commuting and school trips. According the result of regression analysis, population and the number of hospitals and schools area showed positive relationships with but trips reflecting regional characteristics. The research contributes to providing basic data for constructing an efficient public transportation system in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.28-38
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• 2019

The smart card data stores the transit usage records of individual passengers. By using this, it is possible to analyze the traffic demand by station and time. However, since the purpose of the trip is not recorded in the smart card data, the demand for each purpose such as commuting, school, and leisure is estimated based on the survey data. Since survey data includes only some samples, it is difficult to predict public transport demand for each purpose close to the complete enumeration survey. In this study, we estimates the purposes of trip for individual passengers using the smart card data corresponding to the complete enumeration survey of public transportation. We estimated trip purposes such as commute, school(university) considering frequency of O-D, duration, and departure time of a passenger. Based on this, the passengers are classified as workers and university students. In order to verify our methodology, we compared the estimation results of our study with the patterns of the survey data.