A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data

doi:10.3390/ijgi6020045

A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data

2017-02-15

0

OA

AI

DOI：10.3390/ijgi6020045

原文链接

查看原文

摘要

En 中文

With 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.

A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data

摘要

AI总结

期刊

机构

引用论文