Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 71-77, 2016
https://doi.org/10.5194/isprs-archives-XLI-B4-71-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
10 Jun 2016
ROAD NETWORK GENERALIZATION BASED ON FLOAT CAR TRACKING
Cheng Zhou1, Wenjing Li1, and Hongguo Jia2 1College of Resource and Environment, Wuhan University of Science and Technology, China
2Southwest Jiaotong University, China
Keywords: Road network, Dynamic attributes, Mapping,Gathering, Topology Abstract. Road generalization is not only helpful to simplify complicated road networks but can also satisfy the needs of reasonable display of roads under varying scales, thus offering basis for updating and grading urban roads. This paper proposes a selection method for road network generalization by integrating road-associated vehicle trajectory dynamic properties and road features and calculating the importance of urban roads. First of all, the location and motion information of floating vehicles are associated to relevant roads to generate the dynamic properties of roads. Then, the dynamic and static properties of roads are analyzed, and the cluster analysis is conducted to the trajectory points at road intersections to obtain the importance of some road intersections there are vehicles passing by. Afterwards, the weights of roads are calculated using the dominance rough set, the roads are ranked by weight and the practical significance of ranking results is analyzed. Finally, the selection rules for the basic framework of road network are determined to meet with different requirements and guarantee both connectivity and completeness of road networks. The results show that the relative importance of roads is made clear by taking advantage of the rough set and the generalized road network highlights the distribution and connection of urban main roads.
Conference paper (PDF, 4201 KB)


Citation: Zhou, C., Li, W., and Jia, H.: ROAD NETWORK GENERALIZATION BASED ON FLOAT CAR TRACKING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 71-77, https://doi.org/10.5194/isprs-archives-XLI-B4-71-2016, 2016.

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