Name
Abstract | ||
|---|---|---|
A new method for the automatic identification of road geometry from digital vector data is presented. The method is capable of efficiently identifying circular curves with their radii and tangents (straight sections). The average error of identification ranged from 0.01 to 1.30 % for precisely drawn data and 4.81 % in the case of actual road data with noise in the location of vertices. The results demonstrate that the proposed method is faster and more precise than commonly used techniques. This approach can be used by road administrators to complete their databases with information concerning the geometry of roads. It can also be utilized by transport engineers or traffic safety analysts to investigate the possible dependence of traffic accidents on road geometries. The method presented is applicable as well to railroads and rivers or other line features. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10109-016-0230-1 | Journal of Geographical Systems |
Keywords | Field | DocType |
Circular curves, Tangents, Automatic geometry identification, Curvature, Discriminant analysis, Classification tree, Roads, Database, GIS, C8, C18, R41 | Curvature,Vertex (geometry),Radius,Tangent,Linear discriminant analysis,Geometry,Geography,Decision tree learning | Journal |
Volume | Issue | ISSN |
18 | 3 | 1435-5949 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard Andrásik | 1 | 0 | 0.34 |
| Michal Bíl | 2 | 0 | 0.34 |