• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.296-303
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• 2016

In public transportation, smart cards have been introduced for the purpose of convenient payment systems. The smart card transaction data can be utilized not only for the exact and convenient payment but also for civil planning based on travel tracking of citizens. This paper focuses on the analysis of the transportation convenience using the smart card big data. To this end, a new index is developed to measure the transit convenience of each region by considering how passengers actually experience the transportation network in their travels. The movement data such as movement distance, time and amount between regions are utilized to access the public transportation convenience of each region. A smart card data of five working days in March is used to evaluate the transit convenience of each region in Seoul city. The contribution of this study is that a new transit convenience measure was developed based on the reality data. It is expected that this measure can be used as a means of quantitative analysis in civil planning such as a traffic policy or local policy.

• 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 KSR Conference
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• 2011.05a
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• pp.1811-1825
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• 2011

This study analyzed the transit services with respect to transit service measures such as the load factor representing number of passengers between stops, dwelling time, and operational speed based on transit smart card data recorded in 2009. A case study on the local bus line 7024 connecting Seoul railway station to evaluate bus services at passenger perspectives was accomplished. From the results, we found that the dwelling time was not affected by the number of passengers which is because the tagging patterns are different among passengers. The operational speed was analyzed by calculating the average speed of the bus route and the speed of each bus stops based on dwelling time. Interestingly, calculating operation speed based on the transit smart card data is the first time effort ever made and this means that it is not necessary to observe travel speed of bus and railway at a field level any more. we hope that this study will be a basis of evaluation of transit services purely based on the transit smart card data and help to make better transit services for passengers and operators as well.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.563-570
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• 2011

This study analyze the transfer patterns of passengers in Seoul based on transit smart card data that was observed in 2010. The smart card records maximum four times of transfer and reports that approximately 90% of trips were less than one transfer and the remains were more than 2 transfers. We focus on trips with more than 3 transfers to figure out the relationship between transit service and regional connectivities. The results show that the average travel time, distance, fare are 45 minutes, 18.3km, and 1,119(KW) respectively. We develope a map for investigating transfer patterns at a regional level(dong and gu). By doing this, three types of transfers are observed as: 1) trips of which origin and destination is either same or near, 2) trips with short distance, and 3) long distance trip with low transit connectivities.

• 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 Korean Society for Railway
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• v.15 no.1
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• pp.80-87
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• 2012

This study analyzed the quality of a commuter rail service of Incheon line 1 with respect to two service measures such as occupancy (crowdedness) and punctuality based on transit smart card data collected in 2009. In order to analyze the metro services by individual fleet, we aggregated the personal level card data into the fleet operated in each planned schedule. The results show a low level of service for both crowdedness and punctuality during peak hours at the line segment from 'Gyeyang' to 'International business district'. Further, a close relationship between vehicle occupancy and punctuality is found, which illustrates high passenger demand causes successive metro delay.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1699-1706
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• 2011

This study analyzed the level of service on passenger transfer between metro and bus based on transit smart card data obtained in 2010. In order to evaluate the level of service on transfer, we defined the service level specially on transfer time at metro stations. The data of passenger transfer time were used in cluster analysis to classify the service level from A to F. The results show that the average transfer time from metro to bus was 6.45 minutes. The number of stations with level of service A(approximately less than 7 minutes) and B(less than 16minutes) were found to be 215 and 227stations respectively. Also, the number of stations with the level of service C and D (greater than 20 minutes for transfer) were found to be 6 stations where any type of improvement on transfer facilities is required.

• Journal of the Korean Regional Science Association
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• v.33 no.3
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• pp.101-113
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• 2017

This study examines the mobility of the disadvantaged population groups in terms of public transportation using the 2014 smart card data in Seoul, Korea. Particularly, we focus on the disadvantaged population such as senior group, junior group, and low-income population group. Based on the spatial distributions of public transportation mobility levels and the disadvantaged population groups, we identify specific areas where public transportation service should be improved for the disadvantaged population. As a result, we identify 15 administrative-dongs where the ratio of the disadvantaged population is high while the mobility index of public transit is low. The main contributions of this study are as follows. First, we use the smart card data which contains the information of actual trip made by individuals and develop the evaluation process of urban mobility for the disadvantaged population groups. Second, we identify the specific areas where public transportation service should be improved for the different group of the disadvantaged population. Lastly, we discuss policy implications to improve the urban mobility of the disadvantaged population.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.595-600
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• 2011

This study analyzed passenger's access modes that connect to regional railway station and developed a model of access mode choice based on transit smart card data of Seoul station as a case study. The study boundary includes sixteen bus stops around the station. The results show that most passengers access to station have less than two transfers. Of total 15000, eighty percent of passengers use metro and the rest of people take a bus. Interestingly, it is found that almost same proportions of passengers use metro and bus for egress the station. Consequently, metro is found to be most likely used mode compared to bus for both access and egress trips.

• Journal of Korean Society of Transportation
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• v.34 no.5
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• pp.437-448
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• 2016

Some previous researches suggested a transit trip destination inference method by constructing trip chains with incomplete(missing destination) smart card dataset obtained on the entry fare control systems. To explore the feasibility of the transit trip destination inference method, the transit trip chains are constructed from the pre-paid smart card tagging data collected in Busan on October 2014 weekdays by tracing the card IDs, tagging times(boarding, alighting, transfer), and the trip linking distances between two consecutive transit trips in a daily sequences. Assuming that most trips in the transit trip chains are linked successively, the individual transit trip destination zones are inferred as the consecutive linking trip's origin zones. Applying the model to the complete trips with observed OD reveals that about 82% of the inferred trip destinations are the same as those of the observed trip destinations and the inference error defined as the difference in distance between the inferred and observed alighting stops is minimized when the trip linking distance is less than or equal to 0.5km. When applying the model to the incomplete trips with missing destinations, the overall destination missing rate decreases from 71.40% to 21.74% and approximately 77% of the destination missing trips are the single transit trips for which the destinations can not be inferable. In addition, the model remarkably reduces the destination missing rate of the multiple incomplete transit trips from 69.56% to 6.27%. Spearman's rank correlation and Chi-squared goodness-of-fit tests showed that the ranks for transit trips of each zone are not significantly affected by the inferred trips, but the transit trip distributions only using small complete trips are significantly different from those using complete and inferred trips. Therefore, it is concluded that the model should be applicable to derive a realistic transit trip patterns in cities with the incomplete smart card data.