Generating Travel Patterns of Public Transportation Users Using a k-means Clustering Based on Smart Card Data

Lee, Inmook; Hong, Min Jae; Kim, Kyoung‐Tae; Kho, Seung-Young

doi:10.7782/jksr.2020.23.3.204

Cited by 3 publications

(2 citation statements)

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“…Ten, only the travel records in which both the boarding and alighting area codes matched the codes of the 44 dongs in Seongnam were extracted; and there were 1,103,306 trips within Seongnam, suggesting that most trips occurred within the city. Next, to obtain the travel patterns, only the travel records of the elderly who traveled more than three days (10% of the analysis period) were extracted [39]. Finally, abnormal records with missing values or errors were eliminated.…”

Section: Data Preprocessing and Results Of Gmm Applicationmentioning

confidence: 99%

“…As the partitioning clustering algorithm has a linear time complexity with respect to the data size, it can efciently process large datasets [38]. Consequently, it has been applied to various research felds and is widely used in studies involving large smart-card datasets [39,40]. Te most typical partitioning clustering algorithm, the k-means algorithm, rapidly determines cluster membership by minimizing the distance between the central point and the other data points in the cluster.…”

Section: Methodsmentioning

confidence: 99%

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Introducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly

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et al. 2023

Journal of Advanced Transportation

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The transportation-disadvantaged population is rapidly increasing through aging; moreover, the mobility of the elderly has increased due to life expectancy improvements and lifestyle changes. To respond to these changes, a customized mobility service specifically tailored to the travel patterns of the elderly was developed in this study. In particular, an autonomous shuttle service that can improve the operational efficiency and the accessibility of existing transportation methods was proposed by considering the travel characteristics of elderly people, who mainly travel short distances. To this end, a study was conducted in Seongnam City, Republic of Korea, where mobility support services for the elderly are insufficient. Using smart card data, the elderly were classified according to their travel patterns, with autonomous shuttle routes suggested for each travel purpose. We derived four clusters via the Gaussian mixture model clustering, with travel purposes classified according to the spatiotemporal travel patterns. Finally, the major routes for each travel purpose were selected, and feasible road paths for autonomous shuttle operation were suggested using the concept of ODD (operational design domain). This holistic methodology is expected to contribute to the development of autonomous mobility services for the elderly and for the establishment of welfare policies based on the smart card data.

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Section: Data Preprocessing and Results Of Gmm Applicationmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Introducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly

Kang

Park

Seo

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

Analyzing the accessibility of subway stations for transport-vulnerable population segments in Seoul: Case of bus-to-subway transfer

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et al. 2022

Case Studies on Transport Policy

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Anomaly Detection of Photovoltaic Systems Installed in Renewable Energy Housing Support Project Sites by Analyzing Power Generation Data

Kim

Suh

et al. 2022

J Korean Solar Energy

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In this study, we proposed a new method of detecting abnormalities by analyzing power generation data of photovoltaic (PV) systems installed in renewable energy housing support project sites. The study site is north of Gakbuk-myeon, Cheongdo-gun, Gyeongsangbuk-do, Korea, where 63 PV systems have been installed and operated. Based on the system design and surrounding environment, the 63 PV systems were clustered into 6 groups using the K-means clustering method, which is an unsupervised machine learning algorithm. The power production data from the PV systems in each group were analyzed and set as abnormal values if they deviated from the range of ±2.58 times the standard deviation from the mean (assuming a normal distribution and 99% confidence interval). As a result, several abnormalities were detected in the PV systems in November 2020. The cause of the abnormalities was confirmed through site investigation. The proposed method is expected to accelerate the diagnosis of PV systems in renewable energy housing support project sites.

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Generating Travel Patterns of Public Transportation Users Using a k-means Clustering Based on Smart Card Data

Cited by 3 publications

References 0 publications

Introducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly

Introducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly

Analyzing the accessibility of subway stations for transport-vulnerable population segments in Seoul: Case of bus-to-subway transfer

Anomaly Detection of Photovoltaic Systems Installed in Renewable Energy Housing Support Project Sites by Analyzing Power Generation Data

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