Urban Intersection Recognition and Construction Based on Big Trace Data

doi:10.11947/j.AGCS.2017.20160614

Abstract

Abstract: Intersection is an important part of the generation and renewal of urban traffic network. In this paper, a new method was proposed to detect urban intersections automatically from the spatiotemporal big trace data. Firstly, the turning point pairs were based on tracking the trace data collected by vehicles. Secondly, different types of turning point pairs were clustered by using spatial growing clustering method based on angle and distance differences, and the clustering methods of local connectivity was used to recognize the intersection. Finally, the intersection structure of multi-level road network was constructed with the range of the intersection and turning point pairs. Taking the taxi trajectory data in Wuhan city as an example, the experimental results showed that the method proposed in this paper can automatically detect and recognize the road intersection and its structure.

Key words: urban traffic network, automatic intersection recognition, intersection structure, similarity clustering, big trace data

CLC Number:

P208

TANG Luliang, NIU Le, YANG Xue, ZHANG Xia, LI Qingquan, XIAO Shilun. Urban Intersection Recognition and Construction Based on Big Trace Data[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(6): 770-779.

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