Improved Map Construction using Subtrajectory Clustering

Abstract

Map construction is the problem of reconstructing a travel network based on trajectory data of entities travelling on the network. While many map construction algorithms reconstruct the global structure of a network well, local features such as location of crossings and turns are generally harder to reconstruct correctly, in particular for noisy and irregularly sampled data. We demonstrate how subtrajectory clustering can be used to construct maps that capture both the global structure and local features well.