• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.25-35
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• 2008

The ridership and transit systems are influenced by the expansion of metro-rail in Seoul metropolitan area. However, it has been difficult to measure its precise quantitative influences. Also effective policy implementations have been limited due to the lack of practical evidences. Thus an empirical analysis for an operating metro-rail is essentially required. In this regard, this study examines the impact of the Jungang line on transportation system, whose metro-rail block has been recently started new service. The main interest of this study is to find out the changes of ridership and to forecast the ridership changes by the metro-rail service. The results indicate that the effect on auto users is less than that of bus users. The auto demand is decreased by 33.3% and the bus demand by 66.6%. Additionally, its influence on Gyeonggi-do bus was greater compared with that on the Seoul bus. From questionnaire survey, this results could be confirmed. To sum up, the metro-rail gives less influences on auto users, while it gives greater influences on bus users.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.149-150
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• 2002

• International Journal of Human Ecology
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• v.14 no.2
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• pp.79-91
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• 2013

The market for a payday advance, regarded as both a convenient and short term-loan for immediate financial help, has grown incredibly since the 1990's. Despite its popularity by borrowers and the possible benefits, it has received negative publicity. Some borrowers have been caught in a debt trap for a long-term period and at tripledigit interest rates. The objective of this study is to shed light on the borrowers' profiles and their demand for a payday advance. Based on the 2010 household level data from the U.S. Federal Reserve Board, this study finds that payday advance users are pronounced as seemingly risky people. Payday advance users tend to be college drop-outs, African Americans, and non-homeowners compared to non-payday advance users. They are more likely to overspend above their income and have a favorable attitude toward conspicuous spending than non-payday advance users. They tend not to shop at all nor perform even moderate shopping for credit before using a payday advance service as opposed to non-payday advance users.

• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.877-886
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• 2019

The size of the subway entrance should be calculated according to the user's demand, but Korea has the same size for each entrance by applying a uniform value. Recently, the installation of mobile convenience facilities such as escalators, elevators, etc. is mandated by the traffic weakness promotion law, but it is inconvenient to use the existing stations because it is mainly arranged in the place where it can be installed regardless of user demand. This study aims to establish a model for estimating the size of mobile convenience facilities by predicting the use demand of each station entrance so that the location and size of mobile facilities can be reflected in the design or construction of the station. To this end, a multiple regression model was established to forecast daily demand by utilizing the demand for getting on and off by station and the building association area for each purpose around the railway station. The actual data of Dongdaemun and Jonggak Stations were used to verify the estimated model. In addition, the escalator installation scale was compared / analyzed by doorway using domestic and overseas escalator equations. As a result, it was more accurate to estimate the usage demand for a single station. Also, Jonggak Station has an up and down escalator installed at exit 1, but it was analyzed that it is appropriate to install at exit 4. This study is an advanced form of the essay model for estimating the users of the entrance and exit users of urban railway stations published in 2018. In addition, it seems to be the basis of the current escalator installation criteria.

• The Journal of Information Systems
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• v.30 no.4
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• pp.43-69
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• 2021

Purpose Although companies occupied the network market take the advantageous position first and can be successful in securing users over a certain size, it is important to satisfy the customers' demand and prevent the outflow of users toward a new alternative SNS. What is more, there are frequent changes in the flow of users toward new SNSs. Despite these dynamic market circumstances, there is a lack of research to explain the switching behaviors of SNS users. Design/methodology/approach The objective of this study is to explain and verify a specified migration theory(Push-Pull-Mooring model) focused on SNS fatigue in the psychological point of view, as well as reviewing previous studies on functional and technical characteristics of SNSs themselves. Moreover, this study tried to highlight factors affecting users actual SNS switch rather than their switching intention. Findings According to the statistical analysis, the most influenced pull factor to switch SNS was the alternative attractiveness. On the other hand, undesired relationship burden, service innovation and important mooring factors to prevent users' SNS switch. This study has a significant contribution to the theory, which analyzed users' actual SNS switch, and examined SNS users' psychological factors(SNS fatigue), reviewing the characteristics of existing services. To secure more users and to keep them, companies providing social network service need to develop differentiated strategies by analyzing psychological characteristics of various users.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.219-230
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• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.1
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• pp.66-75
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• 2012

The purpose of this study is to analyze the effect of intervention variables which may affect the air travel demand for Jeju domestic flights and to anticipate the air travel demand for Jeju domestic flights. The air travel demand forecasts for Jeju domestic flights are conducted through ARIMA-Intervention Model selecting five intervention variables such as 2002 World Cup games, SARS, novel swine-origin influenza A, Yeonpyeongdo bombardment and Japan big earthquake. The result revealed that the risk factor such as the threat of war that is a negative intervention incident and occurred in Korea has the negative impact on the air travel demand due to the response of risk aversion by users. However, when local natural disasters (earthquakes, etc) occurring in neighboring courtiers and global outbreak of an epidemic gave the negligible impact to Korea, negative intervention incident would have a positive impact on air travel demand as a response to find alternative due to rational expectation of air travel customers. Also we realize that a mega-event such as the 2002 Korea-Japan World Cup games reduced the air travel demand in a short-term period unlike the perception in which it will increase the air travel demand and travel demands in the corresponding area.

• Journal of Korean Society of Transportation
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• v.8 no.1
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• pp.17-24
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• 1990

The first-best pricing rule which achieves economic efficiency is to equate price with marginal cost. Since public transport demand is derived from some other demand, the user cost as well as the producer cost are considered in its pricing. The optimal price is derived from a derivative of the total social cost with respect to demand. In case of the bus, if there is enough capacity for demand increase, the optimal price is determined by the marginal producer cost resulting from bus sped decrease and by the marginal user cost resulting from journey time increase. Both are caused by boarding and fare collecting time of an additional passenger. Because of the budget constraints, the marginal cost pricing cannot be applied in practice. Then price discrimination as the second-best pricing is introduced. The Ramsey pricing, to charge different prices for different demand elasticities, and nonuniform prices such as travelcards can be applied. However, there is practical difficulty in implementing these prices because of great informational requirements, the costs of administration and the ease to users.