Abstract
This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.
Highlights
1.1 Road Network Data and the Lack of Thematic AttributesRoads connect cities, towns, and rural areas, serving transportation needs between settlements and events
Depending on traffic services a road is designated to provide, and geographic extents the road covers, a road network as a whole manifests a strong hierarchical nature, with sparsely distributed high speed roads sitting at the top level and densely clustered local streets forming the bottom of the hierarchy
They will be presented at varying quality statuses, with or without being classified as highways, ramps, major roads, or local streets
Summary
Towns, and rural areas, serving transportation needs between settlements and events. In GIS (Geographic Information Systems) communities, road network datasets are commonly available from well-known sources, such as government agencies or private data providers. They will be presented at varying quality statuses, with or without being classified as highways, ramps, major roads, or local streets. A network dataset is usually populated with piecemeal road segments in need of assembly to be recognized as connected roads. For this kind of raw data containing only geometry, it becomes quite puzzling to know what features belong to a single road. One of the questions to ask is: is it possible to know all these automatically?
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