• Journal of Climate Change Research
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• v.2 no.3
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• pp.191-201
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• 2011

This study investigates how green infrastructure, including natural and open space such as forests, rivers, parks, and streets, could effectively counteract climate change in terms of mitigation and adaption, respectively. As a result, green infrastructure, such as forests, parks, vegetable gardens, roof gardens, pedestrian walkways, bike lanes, etc, could effectively mitigate climate change: 1) Carbon storage and sequestration; 2) Fossil fuel substitution; 3) Material substitution; 4) Food production 5) Reducing the need to travel by car. Secondly, green infrastructure, such as rivers, tree-lined streets, farmland, wetlands, dunes, wind ways, etc, could adapt to climate change: 1) Managing high temperatures; 2) Managing water supply; 3) Managing ravine flooding; 4) Managing costal flooding; 5) Managing surface water; 6) Reducing soil erosion; 7) Helping other species to adapt.

• Journal of Information Technology Services
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• v.20 no.6
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• pp.83-95
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• 2021

In this study, various usage patterns were analyzed after establishing a data mart for micro mobility analysis based on the rental history of public bicycles in Seoul. Rental history data is origin-destination data that includes the rental location and time, and the return location and time. About 1500 rental locations were classified according to the characteristics of the location to create a 'station type' dimension. We also created a 'path type' dimension that displays whether the rental location and return location are the same. In addition, a derived variable called speed, which is obtained by dividing the distance used by the time used, is added, and through this, the characteristics of the riding area and the reason for the rental can be estimated. Meanwhile, administrative district link, administrative neighborhood link, and station type link were created to apply network analysis. Through this analysis, the roles and proportions of administrative districts, public facilities, and private facilities engaged in micro-mobility services were visualized. 49.9% of rentals occur at rental offices near transportation facilities, and half of them occur at rental offices near subway stations. The number of rentals during the evening rush hour is more than double that of the morning rush hour. When the path type is unidirectional, there is a fixed destination, so the distance and time used are short, and the movement speed tends to be high. In the case of round-trip, the purpose of use is exercise or leisure, so the distance and time used are long, and the movement speed is slow. It is expected that the results of the analysis can be used as reference materials for selecting new rental locations, providing convenient services for users, and developing user-specialized products.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.211-220
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• 2011

TOD(Transit Oriented Development) has recently been active, which presents that TOD planning elements should be comprehensively taken into consideration in order to enhance domestic transit ridership by changing environments in rail station areas and an empirical analysis on the type of rail station areas and transportation demand should be a prerequisite for usage of future development planning. This study aims to grasp a variety of TOD of influence factors in Seoul rail station area and to perform analysis to identify relationship between public transportation demand and these TOD design factors. To make it come true, we gathered data with respect to Density, Diversity, and Accessibility as representative TOD planning elements and carried out factorial and regression analysis. Consequently, we drew 7 influence factors base on factorial analysis: Factor 1(Diversity/ -Use Mix(LUM)), Factor 2(Density/development density), Factor 3(Accessibility/public transportation facility supply), Factor 4(Design/street design), Factor 5(Green/access mode (pedestrian, bike), Factor 6(Design/subway size), Factor 7(Accessibility/Public transit operation) As the result of model development by using factorial and regression analysis, positive influence factors on passenger flow in rail station area are Factor 1(Diversity : Land-Use Mix), Factor 3(Accessibility : public transportation facility supply), Factor 2(Density : development density), Factor 5(Design/ access mode) and Factor 6(subway size) Next, negative influence factor on passenger flow in rail station area shows Factor 7(Accessibility/Public transit operation) as the most influential factor. This is because the growth of service interval of linked subway and bus leads to reduced demand.

• The Journal of Bigdata
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• v.7 no.2
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• pp.235-244
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• 2022

The rental history data of public bicycles in Seoul were analyzed to examine how pandemic phenomena such as COVID-19 caused changes in people's micro mobility. Data for 2019 and 2021 were compared and analyzed by dividing them before and after COVID-19. Data were collected from public data portal sites, and data marts were created for in-depth analysis. In order to compare the changes in the two periods, the riding direction type dimension and the rental station type dimension were added, and the derived variables (rotation rate per unit, riding speed) were newly created. There is no significant difference in the average rental time before and after COVID-19, but the average rental distance and average usage speed decreased. Even in the mobility of Ttareungi, you can see the slow rhythm of daily life. On weekdays, the usage rate was the highest during commuting hours even before COVID-19, but it increased rapidly after COVID-19. It can be interpreted that people who are concerned about infection prefer Ttareungi to village buses as a means of micro-mobility. The results of data mart-based visualization and analysis proposed in this study will be able to provide insight into public bicycle operation and policy development. In future studies, it is necessary to combine SNS data such as Twitter and Instagram with public bicycle rental history data. It is expected that the value of related research can be improved by examining the behavior of bike users in various places.