| Co-Investigator(Kenkyū-buntansha) |
IMAI Keiko Chuo University, Fac.Sci.Tech., Prof., 理工学部, 教授 (70203289)
INABA Mary Graduate School of Science, the University of Tokyo Lecturer, 大学院・理学系研究科, 講師 (60282711)
ASAI Ken-ichi Graduate School of Science, the University of Tokyo Research Assistant, 大学院・理学系研究科, 助手 (10262156)
TOKUYAMA Takeshi Tohoku University, GSIS, Prof., 大学院・情報科学研究科, 教授 (40312631)
KUBOTA Koichi Chuo University, Fac.Sci.Tech., Assoc.Prof., 理工学部, 助教授 (90178046)
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| Research Abstract |
In this research, geographical information systems (GIS for short) as well as intelligent transport systems (ITS for short) have been investigated from the viewpoint of computational geometry to meet emerging demands to realize high-quality, robust and fast systems in both fields. From the viewpoint of computational geometry, we also developed efficient geometric clustering algorithms, both in Euclidean and information geometric spaces, and applied them to geographical data mining. Also, map labeling problem and map matching problems are studied.
Specifically, by using robust algorithm to construct the Voronio diagram and Delaunay triangulation of points in the plane, we developed an efficient and simple method of computing the medical axis, as central lines of roads, for town maps. With such nice tools, this kind of seemingly complicated problem can be solved in practice in a very precise manner. Concerning the map labeling problem, subway maps are intensively studied where labels corresponding to each subway line are automatically placed in a beautiful way. Concerning ITS, we have proposed an intelligent algorithm to find meaningful detours for high-level car navigation. Also, a new method of measuring traffic flows from simple sensor data derived at two distant points is presented. The Voronoi diagram has bee used directly in GIS, and we generalize it to the diagram in statistical parameter space. When GIS is combined with other data such as population data, the space becomes higher-dimensional geometric space, to which our generalized diagrams can be used to find proximity relations, etc., by using computational-geometric algorithms.
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