• Journal of the Korean Geographical Society
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• v.41 no.5 s.116
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• pp.582-599
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• 2006

Due to the growing popularity of mobile information technology, more people, especially in the general public, have access to computerized geospatial information systems for wayfinding tasks or urban navigation. One of the problems with the current services is that, whether the users are exploring or navigating, whether they are travelers who are totally new to a region or long-term residents who have a fair amount of regional knowledge, the same method is applied and the direction are given in the same way. However, spatial knowledge for a given urban region expands in proportion to residency. Urban navigation is highly dependent on cognitive mental images, which is developed through spatial experience and social communication. Thus, the wayfinding service for a regional community can be highly supported, using well-known regional places. This research is to develop the framework for urban navigation within a regional community. The concept of communal ontology is proposed to aid in urban regional navigation. The experimental work was implemented with case study to collect regional landmarks, develop the ontological model and represent it with formal structure. The final product of this study will provide the geographical information of a region to the other agent and be the fundamental information structure for cognitive urban regional navigation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.652-657
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• 2014

Given a starting point, destination point and various Points Of Interest (POIs), which contain a full or partial order, for a user to visit we wish to create, a sequenced route from the starting point to the destination point that includes one member of each POI type in a particular order. This paper proposes a method for finding the approximate shortest route between the start point, destination point and one member of each POI type. There are currently two algorithms that perform this task but they both have weaknesses. One of the algorithms only considers the distance between the visited POI (or starting point) and POI to visit next. The other algorithm chooses candidate points near the straight-line distance between the start point and destination but does not consider the order of visits on the corresponding network path. This paper outlines an algorithm that chooses the candidate points that are nearer to the network path between the start point and destination using network search. The algorithm looks for routes using the candidate points and finds the approximate shortest route by assigning an adaptive priority to the route that visits more POIs in a short amount of time.