• Survey Research
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• v.12 no.2
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• pp.27-50
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• 2011

It is increasingly important to estimate daytime population for the sake of urban administration and urban economy. The estimation of daytime population using a census data on commute, however, has its own limits, particularly when applying to the metropolis such as Seoul which is dominated by the service industry. This study suggests a pedestrian flow as another estimate of daytime population and presents a methodology of Seoul Pedestrian Flow Survey. The data of pedestrian flow gives us a view into hourly and spatial distribution of daytime population, which cannot be provided by the use of census data. In addition, comparing with a census-based daytime population on the borough level show some features of a pedestrian flow as another estimate of daytime population.

• Journal of KIBIM
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• v.9 no.1
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• pp.22-31
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• 2019

In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data (total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.1
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• pp.145-151
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• 2008

A novel method was suggested for the estimation of design population and design wastewater flow rate in fishing and agrarian village. Even though the population was decreasing continuously in this area, the design population was considered as constant with the passage of the time in conventional methods. And although the portion of groundwater uses was pretty high, the design wastewater flow rate was determined by the supply amount of tap water. Consequently, the design population and the design wastewater flow rate were overestimated. To prevent these overestimates, the design population was predicted to decrease gradually using the population trends from Korea National Statistical Office, and the design wastewater flow rate was determined using the way that the supply amount of tap water was applied in developed areas and the supply amount of groundwater was used in undeveloped areas.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.6
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• pp.877-884
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• 2011

Although peak loading coefficient is one of critical design factors for sewer works, its detailed affecting factors were not analyzed because of limited data availability. This study analyzed the affecting factors on peak loading coefficient with plenty data obtained from several newly constructed sewer works. Simple and multiple regression analysis methods were adopted to analyze the relationships of each variable with or without data filtering. Drainage population, drainage area, population density, and daily sewage flow per person showed very weak relationships under diverse characteristics of cities. However, daily sewage flow per person showed stronger relationships with peak loading when daily sewage flow per person was splitted into two ranges. Population density (i.e., drainage population divided by drainage area) and daily sewage flow per person considerably were related with peak loading coefficient when daily sewage flow per person is less than about 400 Lpcd.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.6
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• pp.749-757
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• 2005

The present study analyzed actual cases of designed flow estimation method and designed flow rate of sewage pipe lines. In order to examine the effects of peak-hour demand factor estimation with given daily highest peak loading, we analyzed its effects on designed flow rate with changing the peak-hour demand factor from 2.0 to 10.0. The results of this study are as follows. When reviewing the recent designs, we found that 59.4% of pipe line with 250mm and 300mm diameter, which fall under minimum allowable pipeline did not meet the minimum velocity which is specified as 0.6m/sec in design standards. The pipe line that have minimal access population or have very low slope did not satisfy the minimum velocity. In estimating the designed sewage flow, the applied daily highest peak loading and hourly highest peaking loading were the load factor for the entire population of the planned area, and for the peak loading of the initial pipes connected to a very small population, we applied the same factor as that applied to the entire area and, as a result, the hourly highest flow was underestimated. Because, in case of the initial pipes, the method of applying the same peak loading to all subject areas is highly possible to produce underestimated design flow, when estimating the designed flow of the initial pipes connected to a small population need to adopt a rational flow factor according to the size of population. For this, it is considered to investigate and analyze raw data on daily and hourly variation of sewage flow.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3197-3202
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• 2007

An analysis program for pedestrian flow has been developed to investigate the flow patterns of passenger in railway stations. Analysis algorithms for pedestrian flow based on DEM(Discrete Element Method) are newly developed. There are lots of similarity between particle-laden two phase flow and passenger flow. The velocity component of 1st phase corresponds to the unit vector of calculation cell, each particle to passenger, volume fraction to population density and the particle velocity to the walking velocity, etc. And, the walking velocity of passenger is also represented by the function of population density. Key algorithms are developed to determine the position of passenger, population density and numbering to each passenger. By using the developed program, we compared the simulation results of the effects of the location and size of exit and elapsed time.

• Journal of the Korean Society for Railway
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• v.8 no.4
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• pp.337-341
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• 2005

Algorithm for passenger flow analysis based on DEM(Discrete Element Method) is newly developed. In the new algorithm, there are many similarity between multi phase flow and passenger flow. The velocity component of 1st phase corresponds to the direction vector of cell, each particle to each passenger, volume fraction to population density and the momentum equation of particle to the walking velocity equation of passenger, etc. And, the walking velocity of passenger is also represented by the function of population density. Key algorithms are developed to determine the position of passenger, population density and numbering to each passenger, To verify the effectiveness of new algorithm, passenger flow analysis for simple railway station model is conducted. The results for passenger flow in the model station are satisfying qualitatively and quantitatively.

• Journal of the Korean Society for Railway
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• v.9 no.3 s.34
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• pp.271-276
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• 2006

Insight into behaviour of pedestrians as welt as tools to assess passenger flow condition is important in such instances as planning and geometric design of railway station under regular and safety-critical circumstances. Algorithm for passenger flow analysis based on DEM (Discrete Element Method) is newly developed. There are lots of similarity between particle-laden two phase flow and passenger flow. The velocity component of 1st phase corresponds to the unit vector of calculation cell, each particle to passenger, volume fraction to population density and the particle velocity to the walking velocity, etc. And, the walking velocity of passenger is also represented by the function of population density. Key algorithms are developed to determine the position of passenger, population density and numbering to each passenger. To verify the effectiveness of new algorithm, passenger flow analysis for the basic models of railway station is conducted.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.387-391
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• 2006

Insight into behaviour of pedestrians as well as tools to assess passenger flow conditions are important in for instance planning and geometric design of railway station under regular and safety-critical circumstances. Algorithm for passenger flow analysis based on DEM(Discrete Element Method) is newly developed. There are lots of similarity between particle-laden two phase flow and passenger flow. The velocity component of 1st phase corresponds to the unit vector of calculation cell, each particle to passenger, volume fraction to population density and the particle velocity to the walking velocity, etc. And, the walking velocity of passenger is also represented by the function of population density. Key algorithms are developed to determine the position of passenger, population density and numbering to each passenger. To verify the effectiveness of new algorithm, passenger flow analysis for the basic models of railway station is conducted.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.181-195
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• 2019

This study aims to analyze the spatial context by analyzing the flow characteristics of the walking tour course and visualizing effectively using the floating population data constructed through the communication data. The floating population data refinement algorithm was developed for estimation flow population along the road and the floating population data for each walking tour courses was constructed. In order to adopt the algorithm for forming suitable for the analysis of the walking tour courses, the estimation of floating population considering the area of the road and the estimation of floating population considering the value of floating population around the road were compared. As a result, the estimation of floating population considering ambient the values of flow population was adopted, which is more appropriate to apply analysis method due to the relatively consistent data. Then, a datamining algorithm for walking tour course was constructed according to the characteristics of the floating population data, the absence of missing values. Finally, this study analyzed the flow characteristics and spatial patterns of 18 walking trails in Seoul through the floating population data according to walking tour course. To do this, the kernel density analysis and the Getis-Ord $G^*_i$ statistical hotspot analysis were applied to visualize the main characteristics of each walking tour course.