Abstract
This paper proposes a novel approach to detect road intersections from GNSS traces. Different from the existing methods of detecting intersections directly from the road users’ turning behaviors, the proposed method detects intersections indirectly from common sub-tracks shared by different traces. We first compute the local distance matrix for each pair of traces. Second, we apply image processing techniques to find all “sub-paths” in the matrix, which represents good alignment between local common sub-tracks. Lastly, we identify the intersections from the endpoints of the common sub-tracks through Kernel Density Estimation (KDE). Experimental results show that the proposed method outperforms the traditional turning point-based methods in terms of the F-score, and our previous connecting point-based method in terms of computational efficiency.
Highlights
Automatic road map generation plays an important role in vehicle navigation systems [1,2,3,4,5], intelligent traffic control [6,7,8,9,10], urban planning [11,12], etc
We applied our proposed method on a dataset of campus shuttle traces from the University of Illinois at Chicago (UIC) [25]
Most of the connecting points are located at the road intersections, while some of them are on the road segments because of Global Navigation Satellite System (GNSS) errors
Summary
Automatic road map generation plays an important role in vehicle navigation systems [1,2,3,4,5], intelligent traffic control [6,7,8,9,10], urban planning [11,12], etc. The intersections in the road network provide very useful information, such as connectivity, topology and allowable moving direction [13,14,15]. Detecting intersections before road map generation benefits building the topology of the road network. With the advancement of Global Navigation Satellite System (GNSS) technology in the last few decades, it has been used ubiquitously, such as in mobile phones, wearables, watches, navigation systems, etc. This generates enormous GNSS-derived trace data from a variety of road users.
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