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Proceedings Paper

A non-planar data model for road networks based on GIS-T
Author(s): Kehui Xiao; Jianhong Chen; Deqin Xiao; Shengtao Ye
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Paper Abstract

Previous data models of road networks include planar and non-planar models. In planar models, intersections of road networks are transformed into arcs, sections into nodes, and sequential pairs are used to represent arcs between nodes, which can simulate traffic direction of sections and turning limitation at intersections well, but fail to define the granularity of lane and the node height difference. Non-planar data models provide the data of node height difference, which avoid impossible turning in three-dimensional road networks, but shortages as lack of detailed information of road intersections and node height difference and the curtly defined granularity of lanes lead to the low efficiency of analysis of road networks. This paper proposes an NA+ model of road networks based on non-planar structure and combined node height difference with direction lane to describe non-planar structure of road networks. Node height difference is denoted as Arabic numerals, the granularity of lane is specified as direction lane. In GIS-T database, the intersection table and the direction lane table are used to describe node height difference and direction respectively. The model consists with the circumstance of road networks; it is proved to be applicable and feasible to the applications of GIS-T.

Paper Details

Date Published: 5 November 2008
PDF: 8 pages
Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 71442S (5 November 2008); doi: 10.1117/12.812849
Show Author Affiliations
Kehui Xiao, South China Agricultural Univ. (China)
Jianhong Chen, South China Agricultural Univ. (China)
Deqin Xiao, South China Agricultural Univ. (China)
Shengtao Ye, Zhongshan Univ. (China)

Published in SPIE Proceedings Vol. 7144:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang; Xinhao Wang, Editor(s)

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