A network-constrained clustering method for bivariate origin-destination movement data

Abstract

For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services.

Keywords:

• Origin-destination movement data
• bivariate clustering
• road network
• spatial heterogeneity

Acknowledgements

The authors gratefully acknowledge the comments from the editor and the reviewers.

Author contributions

Wenkai Liu and Qiliang Liu conceived and designed the presented idea. Wenkai Liu implemented the experiments and analysed the results. Qiliang Liu and Wenkai Liu wrote the manuscript. Min Deng reviewed the manuscript, and provided comments. Jie Yang collected the research data and checked the code of the proposed method.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available on ‘figshare.com,’ with the identifier at the public link: https://doi.org/10.6084/m9.figshare.19407614.v1

Additional information

Funding

This study was funded through support from National Natural Science Foundation of China (NSFC) [No. 41730105, 42271484, and 41971353] and Natural Science Foundation of Hunan Province [No. 2021JJ20058 and 2020JJ4695]

Notes on contributors

Wenkai Liu

Wenkai Liu is currently a Distinguished Research Fellow at South China Normal University. He received his PhD in GIScience from Central South University. His research interests include spatial data mining and remote sensing change detection.

Qiliang Liu

Qiliang Liu is currently an associate professor at Central South University, Hunan, China. His research interests focus on multi-scale spatio-temporal data mining and spatiotemporal statistics. He has published more than 30 peer-reviewed journal articles in these areas.

Jie Yang

Jie Yang is currently a Ph.D. candidate at Central South University and his research interests focus on spatio-temporal clustering.

Min Deng

Min Deng is currently a professor at Central South University and the associate dean of School of Geosciences and info-physics. His research interests are map generalization, spatio-temporal data analysis and mining.