Inferring Directed Road Networks from GPS Traces by Track Alignment

ISPRS International Journal of Geo-Information

Xingzhe Xie²  Kevin Bing-Yung Wong¹  Hamid Aghajan²  Peter Veelaert²  Wilfried Philips² 

[1] Ambient Intelligence Research (AIR) Lab, Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; E-Mail:;TELIN-IPI-IMINDS, Ghent University, Sint-Pietersnieuwstraat 41, Ghent 9000, Belgium; E-Mails:

关键词: map inference;    intersection extraction;    track alignment;   

DOI  :  10.3390/ijgi4042446

来源: mdpi

【 摘 要 】

This paper proposes a method to infer road networks from GPS traces. These networks include intersections between roads, the connectivity between the intersections and the possible traffic directions between directly-connected intersections. These intersections are localized by detecting and clustering turning points, which are locations where the moving direction changes on GPS traces. We infer the structure of road networks by segmenting all of the GPS traces to identify these intersections. We can then form both a connectivity matrix of the intersections and a small representative GPS track for each road segment. The road segment between each pair of directly-connected intersections is represented using a series of geographical locations, which are averaged from all of the tracks on this road segment by aligning them using the dynamic time warping (DTW) algorithm. Our contribution is two-fold. First, we detect potential intersections by clustering the turning points on the GPS traces. Second, we infer the geometry of the road segments between intersections by aligning GPS tracks point by point using a “stretch and then compress” strategy based on the DTW algorithm. This approach not only allows road estimation by averaging the aligned tracks, but also a deeper statistical analysis based on the individual track’s time alignment, for example the variance of speed along a road segment.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.