Analysis of Regional Transit Convenience in Seoul Public Transportation Networks Using Smart Card Big Data

Moon, Hyun-Koo; Oh, Kyuhyup; Kim, SangKuk; Jung, Jae‐Yoon

doi:10.7232/jkiie.2016.42.4.296

Cited by 5 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A multi-modal public transportation system is well established. Buses and subways operate in most areas of the city, making it easy to commute [31,32]. Although Seoul is mostly built up to its capacity, it supplies abundant urban leisure amenities and the opportunities for physical activities to its citizens, taking advantage of its rich natural resources.…”

Section: Study Sitementioning

confidence: 99%

“…The Han River divides the city into two parts, both geographically and socially: older towns are located north of the river, and the newer and wealthier neighborhoods are located south of the river [32,35]. Because the northern part of Seoul has been developed as an administrative and economic core of the city for about six hundred years, it retains a historic urban form represented as small-grained and organic street networks and block systems [36].…”

Section: Study Sitementioning

confidence: 99%

See 1 more Smart Citation

Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

Choi

Yoon

2020

IJERPH

View full text Add to dashboard Cite

This study investigates the impact of neighborhood-built environments on obesity in interrelationship with socioeconomic status (SES)—controlling for dietary patterns and physical activities of residents—using structural equation modeling (SEM). A total of 577 samples who are between 19 and 64 years old and reside in Seoul are extracted from Korea National Health and Nutrition Examination Survey (KNHNES), 2015. Neighborhood environments are represented as the two latent constructs—walkability and leisure amenities—composited with indicators such as density of intersections, density of mixed-use area, and the area of open spaces and are aggregated by jurisdictional unit in Seoul. We found that greater walkability in a neighborhood explained a lower body mass index (BMI) among residents, whereas more urban leisure amenities in a neighborhood explained a higher BMI. The finding suggests that a walking-friendly environment is more effective than active recreational amenities in inducing people to engage in daily physical activities to the level that reduces obesity rate. SES exerted a negative impact on BMI of a greater magnitude than the impact of either of the environmental living conditions, reinforcing the importance of general wealth and education level in leading to a healthy lifestyle. Our research contributes to growing evidence of a relationship between obesity and the built environment in the context of Asian countries where the prevalence of obesity is becoming a serious issue and requires immediate attention.

show abstract

Section: Study Sitementioning

confidence: 99%

Section: Study Sitementioning

confidence: 99%

Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

Choi

Yoon

2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…Finally, a multimode traffic forecasting model was established on this basis. Moon et al used Seoul smart card data to measure the traffic convenience of each region by considering the actual experience of passengers in the travel network [7]. Kim et al used Seoul's bus IC card data to introduce a sticky index to quantify the user's preference range and to measure the degree of habitual behavior of individuals and bus routes [8].…”

Section: Introductionmentioning

confidence: 99%

OD Matching of Metro IC Card Data Based on Analysis Function

Ding

Wang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In the case of passengers taking the subway many times in a short time, missing cards in and out of the station, and staying in the subway station for a long time, the previous table join method cannot accurately set the time threshold parameters and correctly match the OD pairs of passengers. In order to solve these problems, an OD matching method based on analysis function is proposed in this paper. LAG () is an analytic function in Oracle which allows you to access the row at a given offset prior to the current row without using a self-join. Metro IC card dataset stores the card swiping records of passengers entering and leaving the subway station every time. In this method, the dataset is sorted in ascending order according to the card number and card swiping time, and then, the lag function of Oracle is used to take the offset of the upper line of card ID, transaction date, transaction time, in and out sign, and station ID. Finally, the matching process is completed according to the OD conditions of card number, time, and inbound and outbound sign fields. This method does not need to set a time threshold and so as to deal with the situation where passengers stay too long in the subway station. The OD matching results on in and out IC swiping cards dataset in April and May 2019 of passengers of Xiamen Metro Line verify that analysis function method has better OD matching, missing swiping identification accuracy, and effect compared to the table join method.

show abstract

“…The big data generated by smart cards can be used for the correlation analysis of public transportation lines. Moon H used Seoul smart card data to measure the traffic convenience of each region by considering the actual experience of passengers in the travel network [1]. Kim J used Seoul's bus smart card data to introduce a sticky index to quantify the user's preference range, and to measure the degree of habitual behavior of individuals and bus routes [2].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection of Passenger OD on Nanjing Metro Based on Smart Card Big Data

et al. 2019

View full text Add to dashboard Cite

Urban metro alleviates traffic pressure and also faces safety management problems. The metro AFC (Automatic Fare Collection System) records the OD (Origin-Destination) data of passengers' daily trips. Many researches often neglect the pretreatment of data cleaning based on smart card data. Anomaly OD records also reflect the safety problems. How to use OD to identify anomalous data and passengers' anomalous behavior is a research hotspot of metro big data. OD data of Nanjing metro were analyzed, and standard data cleaning processes were proposed including inbound records until the day before yesterday, inbound records of next days, negative records and overtime records. Then, using the data after cleaning, we analyze long-time records, short-time records, inbound and outbound records between the same stations, the swiping card records of more times, and carry out analysis. One day is chosen as an example to illustrate the analysis process, and then the OD records of several days are compared to summarize the classification of OD anomalies. Through analysis, OD anomalies can be classified into two categories: system anomalies and passenger behavior anomalies. System anomalies can be eliminated by upgrading. Abnormal passenger behavior reflects some potential safety problems. This research can effectively identify the abnormal behavior of passengers by tracking and comparing the appearing frequency of passenger cards. OD anomaly classification can be further refined, so that it has more practical value, can improve the level of metro safety management.

show abstract

Analysis of Regional Transit Convenience in Seoul Public Transportation Networks Using Smart Card Big Data

Cited by 5 publications

References 8 publications

Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

OD Matching of Metro IC Card Data Based on Analysis Function

Anomaly Detection of Passenger OD on Nanjing Metro Based on Smart Card Big Data

Contact Info

Product

Resources

About