A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data
AbstractWith the rapid development of urban transportation, people urgently need high-precision and up-to-date road maps. At the same time, people themselves are an important source of road information for detailed map construction, as they can detect real-world road surfaces with GPS devices in the course of their everyday life. Big trace data makes it possible and provides a great opportunity to extract and refine road maps at relatively low cost. In this paper, a new refinement method is proposed for incremental road map construction using big trace data, employing Delaunay triangulation for higher accuracy during the GPS trace stream fusion process. An experiment and evaluation were carried out on the GPS traces collected by taxis in Wuhan, China. The results show that the proposed method is practical and improves upon existing incremental methods in terms of accuracy. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Tang, L.; Ren, C.; Liu, Z.; Li, Q. A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data. ISPRS Int. J. Geo-Inf. 2017, 6, 45.
Tang L, Ren C, Liu Z, Li Q. A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data. ISPRS International Journal of Geo-Information. 2017; 6(2):45.Chicago/Turabian Style
Tang, Luliang; Ren, Chang; Liu, Zhang; Li, Qingquan. 2017. "A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data." ISPRS Int. J. Geo-Inf. 6, no. 2: 45.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.