  Origin-Destination (OD) flow, as an abstract representation of the object`s
movement or interaction, has been used to reveal the urban mobility and
human-land interaction pattern. As an important spatial analysis approach, the
clustering methods of point events have been extended to OD flows to identify
the dominant trends and spatial structures of urban mobility. However, the
existing methods for OD flow cluster-detecting are limited both in specific
spatial scale and the uncertain result due to different parameters setting,
which is difficult for complicated OD flows clustering under spatial
heterogeneity. To address these limitations, in this paper, we proposed a novel
OD flows cluster-detecting method based on the OPTICS algorithm which can
identify OD flow clusters with various aggregation scales. The method can
adaptively determine parameter value from the dataset without prior knowledge
and artificial intervention. Experiments indicated that our method outperformed
three state-of-the-art methods with more accurate and complete of clusters and
less noise. As a case study, our method is applied to identify the potential
routes for public transport service settings by detecting OD flow clusters
within urban travel data.
