Clustering trajectories for map construction
Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances …, 2017•dl.acm.org
We propose a new approach for constructing the underlying map from trajectory data. Our
algorithm is based on the idea that road segments can be identified as stable subtrajectory
clusters in the data. For this, we consider how subtrajectory clusters evolve for varying
distance values, and choose stable values for these. In doing so we avoid a global proximity
parameter. Within trajectory clusters, we choose representatives, which are combined to
form the map. We experimentally evaluate our algorithm on vehicle and hiking tracking data …
algorithm is based on the idea that road segments can be identified as stable subtrajectory
clusters in the data. For this, we consider how subtrajectory clusters evolve for varying
distance values, and choose stable values for these. In doing so we avoid a global proximity
parameter. Within trajectory clusters, we choose representatives, which are combined to
form the map. We experimentally evaluate our algorithm on vehicle and hiking tracking data …
We propose a new approach for constructing the underlying map from trajectory data. Our algorithm is based on the idea that road segments can be identified as stable subtrajectory clusters in the data. For this, we consider how subtrajectory clusters evolve for varying distance values, and choose stable values for these. In doing so we avoid a global proximity parameter. Within trajectory clusters, we choose representatives, which are combined to form the map. We experimentally evaluate our algorithm on vehicle and hiking tracking data. These experiments demonstrate that our approach can naturally separate roads that run close to each other and can deal with outliers in the data, two issues that are notoriously difficult in road network reconstruction.
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