A.O. Dogru1, N. Van de Weghe2, N.N. Ulugtekin1, P. De Maeyer2

1 - Istanbul Technical University, Cartography Division, Istanbul, Turkey

2 - Ghent University, Department of Geography, Gent, Belgium


Navigation is one of the fundamental human activities. Thanks to the technological developments in computer and electronic sciences, navigation has become an integral part of everyday life. In addition to the technical components, maps are considered as the basic component of navigation systems. Moreover, database and visualisation models, which form the basic infrastructure of these maps, are another fundamental component of these systems. These maps should be considered in two different dimensions: the base map data which involve the road network data on which network algorithms, such as finding shortest path and optimal way, are executed, and the maps which are used as user interface. Since the display media of the navigation systems are small in size, it is impossible to present the base map data as such the user interface during the navigation process. Therefore, multiple representations of the base maps should be used in navigation processes. These different representations should be derived from the base map according to the aim, scale and resolution of the navigation task.

In this study, car navigation is considered as the basic activity and the multiple representation approach for the production of the navigation maps is taken into account to derive road network data for different representation levels of car navigation maps. In this context, road junctions which are the most complicated elements of the road networks are classified considering two different approaches, an approach based on cartographic aspects and an approach based on algorithmic aspects. In the first approach, road junctions are classified according to their aspects at different representation levels. In this concept, three representation levels, which are planned to be derived from 1:5000 scaled base level, are used and representations of possible road junctions are structured according to characteristics of these levels. Finally, they are classified by considering their geometrical shapes in each level. In the second approach, road junctions are classified according to algorithmic concerns. Junctions are defined in matrices based on their specific characteristics, and these matrices are classified according to their similarity. Finally, common representations are determined for these classes.

As a conclusion, this paper introduces the comparison of the cartographic and algorithmic approaches for the classification of road junctions. Since the representation of junctions is the core problem for map production in terms of multiple representations, this study has a significant importance from a cartographic point of view with respect to the car navigation process.