• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.979-994
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• 2008

Moving objects are spatio-temporal data that change their location or shape continuously over time. Generally, if continuously moving objects are managed by a conventional database management system, the system cannot properly process the past and future location which is not stored in the database. Up to now, for the purpose of location tracking which is not stored, the linear interpolation to estimate the past location has been usually used. It is suitable for the moving objects on linear route, not curved route. In this paper, we propose a past location tracking algorithm for a moving object on curved routes, and also suggest a future location tracking algorithm using some past location information. We found that the proposed location tracking algorithm has higher accuracy than the linear interpolation function.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.1-13
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• 2007

The necessity of future index is increasing to predict the future location of moving objects promptly for various location-based services. A representative research topic related to future index is the probability trajectory prediction technique that improves reliability using the past trajectory information of moving objects in the road network environment. However, the prediction performance of this technique is lowered by the heavy load of extensive future trajectory search in long-range future queries, and its index maintenance cost is high due to the frequent update of future trajectory. Thus, this paper proposes the Probability Cell Trajectory-Tree (PCT-Tree), a cell-based future indexing technique for efficient long-range future location prediction. The PCT-Tree reduces the size of index by rebuilding the probability of extensive past trajectories in the unit of cell, and improves the prediction performance of long-range future queries. In addition, it predicts reliable future trajectories using information on past trajectories and, by doing so, minimizes the cost of communication resulting from errors in future trajectory prediction and the cost of index rebuilding for updating future trajectories. Through experiment, we proved the superiority of the PCT-Tree over existing indexing techniques in the performance of long-range future queries.

• Journal of Korea Planning Association
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• v.54 no.7
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• pp.58-69
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• 2019

This study suggested the necessity of introducing the future-oriented new industrial complex as a way to enhance the competitiveness of the industrial complex in response to the industrial ecosystem change caused by the 4th industrial revolution. To evaluate the location suitability for the new industrial complex, location indices were developed in this study. The flexibility of location and connectivity to neighboring areas of the future-oriented new industrial complexes were the focused subjects of this study. In-depth expert consultation and Delphi survey were conducted to construct an index system and to measure the indices. Afterwards, the AHP analysis was conducted to elucidate the weighting by indicators. As a result, the indicators of industrial innovation environment were the most important in the major category. In addition, the convenience of workers, especially urban environment, was analyzed as an important factor for success of the future-oriented new industrial complex. The indicators which are closely related to the concept and purpose of the future-oriented new industrial complex turned out to have the higher weight compared to other indicators. From the point of view of the developers of the industrial complexes or tenant companies, the implications for deriving suitable locations for development of industrial complexes among urban under-utilized sites are suggested. Furthermore, It is expected that the government will be able to apply the development system to stimulate the development of these new industrial complexes and to prepare guidelines for selecting locations.

• Journal of KIISE:Databases
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• v.29 no.6
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• pp.441-452
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• 2002

If continuous moving objects are managed by conventional database, it is not possible for them to store all position information changed over time in the database. Therefore, a time period of regular rate is determined and position information of moving objects are discretely stored in the system for every time period. However, if continuous moving objects are managed as discrete model, we will have problems which cannot properly answer to the query about uncertain past or future position information. To solve this problem, in this paper, we propose the method and algorithm which use the history information stored in the same database, to estimate the past or future location of moving objects. The cubic spline interpolation is used to estimate the past location and the mean movement value of the history information is used to predict the future location of moving objects. Finally, from the location estimation experimentation of using virtual trajectory and location sample, we proved that the proposed cubic spline function has less error than the linear function.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.827-836
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• 2002

Moving object management systems manage spatiotemporal data, which change their location over tine such as people, animals, and cars. These moving object management systems can be applied to vehicle location tracking, digital battlefield, location-based service, and so on. The existing moving object management systems only manage past or future location of the moving objects separately. Therefore, they cannot suggest estimation method of uncertain past or future location of the moving objects. In this paper, we propose a moving object management system, which not only manages historical data of the moving objects, but also predicts past and future location of the moving objects using historical data stored in database. We define the moving objects for vehicle location tracking and propose a moving object database structure. Finally, we suggest an execution model of the proposed system and apply the execution model to a virtual scenario for vehicle tracking.

• Proceedings of the KSR Conference
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• 2002.05a
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• pp.116-121
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• 2002

This primary study has some bounds md limits owing to the descriptive reviews but not detailed analyses. However, 1 tried to do the comparative analyses and case studies about location and size of rail station for future efficient use of spatial structure. Therefore, I reviewed the location of railway station and some design standards of railway station facilities by type (high speed rail, conventional rail and urban rail), and also, suggested future direction for rail station as the core of city development relating with urban structures and human settlement system reformation.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.105-116
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• 2001

Moving objects are spatiotemporal data that location and shape of spatial objects are changed continuously over time. If spatiotemporal moving objects are managed by conventional database system, moving objects management systems have two problems as follows. First, update for location information changed over time is occurred frequently. Second, past and future information of moving objects are not provided by system because only current state of objects is stored in the system. Therefore, in this paper, we propose a spatiotemporal moving objects management system which is able to not only manage historical information of moving objects without frequent update, but also provide all location information about past, current, and near future. In the proposed system, information of moving objects are divided into location information for representing location and motion information for representing moving habits. Especially, we propose the method which can search location information all objects by use of changing process algorithms with minimum history information. Finally, we applied the proposed method to battlefield analysis system, as the result of experiment, we knew that past, current, and near future location information for moving objects are managed by relational database and GIS system.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.72-75
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• 2005

Uncertainty of objects in Moving Object Database is a coherent property. It has been discussed in a lot of researches on modelling and query processing. The previous studies assume that uncertain future time is determined through utilizing recent speed and direction of vehicles. This method is simple and useful for estimating the time of the near future location. However, it is not appropriate when we estimate the time of the far future location. Therefore, in this paper, we propose a concept of planned route. It is used to estimate uncertain future time, which has to be located at a given point. If the route of an object is planned beforehand its locations are uncertainly distributed near that route. By a simple projection operation, the probability that a location lies in the planned route is increased. Moreover, we identify the future time of an object based on the speed for passing the route, which is offered via a website.

• Industrial Engineering and Management Systems
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• v.15 no.3
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• pp.197-205
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• 2016

The public services closely related to the daily lives of the Japanese people, such as firefighting, police or primary school education, are largely financed by the local governments. As the population as a whole in Japan declines, the population in local regions are forecasted to experience particularly rapid decline in the future, and it is inevitable to reduce the cost of public services provided by the local governments to keep their financial basis sustainable. In order to provide public services to the people properly and fairly, the local governments own and utilize their public facilities, such as fire stations, police stations or primary schools. On the other hand, we have to secure the accessibility, which is the condition of accessing a facility easily in a whole local city including the high population density area and low population density area. In this paper, we propose a method of determining the number of future facilities and its facility locations in which we maintain the present accessibility. In our proposed method, we determine them comparing the accessibility measurement calculated by facility location model using the present and future population. We adopted k-centdian model as the facility location model, which can secure the accessibility in a whole local city determining the weights of both areas. We applied our proposed method to fire station in Iwaki city, Japan. The results suggested that 7 facilities would be reduced in 2064, after 50 years from 2014. Additionally, we confirmed that the future facility location had secured accessibility in both high and low population density area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.