An adaptive Origin-Destination flows cluster-detecting method to identify urban mobility trends
This addresses the challenge of analyzing complex urban travel data for applications like public transport planning, though it is incremental as it builds on existing clustering approaches.
The paper tackled the problem of identifying urban mobility trends by clustering Origin-Destination (OD) flows, proposing a method that adaptively determines parameters and outperformed three state-of-the-art methods with more accurate and complete clusters and less noise.
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.