关键词: OD流向, 时空联合聚类, 时空相似性度量, 逐级合并策略, 层次聚类, 时空尺度, 移动模式, 空间联系

Abstract:

Most of the existing OD flow clustering methods adopt the strategy of dividing the OD flow into O point and D point or considering flow as the four-dimensional point to implement flow clustering, which ignores the effects caused by the length, direction and time information on the clustering process. In this paper, we proposedabrand-new spatio-temporal flow clustering method based on the similarity between flows with a strategy of merging flow clusters under different grading. Firstly, a reasonablespatio-temporal similarity measurement formula of OD flow was constructed to quantify the spatio-temporal similarity between OD flows on the basis of full stydy of OD flow's spatial information and temporal information. Then, with the purpose of optimizing the order of merging flow clusters, reducing the time consumption of clustering process, a strategy of merging flow clusters under different grading was used to complete flow clustering. In this method, both of time information and spatial information weretaken into consideration. By modifying the parameters of the spatio-temporal similarity measurement formula, our method can obtain clustering results for different time scales and spatial scales, which makes it possible to analyze the movement patterns from a multi-scale perspective. To verify the effective of our method, a series of experiments on real dataset was executed. The clustering results demonstrate that: ①flow clusters discovered by our method not only hadspatial characteristic but also hadtemporal characteristic; ② our method can discover different spatio-temporal OD flow cluster under different spatio-temporal parameters; ③ by comparingthe clustering results of our method with previous work of advanced technology level, it turnedout that our method hada better clustering performance, which was reflected in the fact that flows within the same flow cluster satisfied the similarity relationship and our method can not only find the obvious movements patterns but also capture inconspicuous movements patterns between non-hot zones. Thespatio-temporal joint OD flow clustering method proposed in this paper obtains new insights into motion from the perspective of joint temporal and spatial information, which is conducive to a reasonable and comprehensive study of residents' movement patterns, spatial linkage between regions, the determination of the known travel structure, and the exploration of the purpose of travel. The process of OD flow clutsering is the beginning of a series of subsequent analysis.

Key words: OD flow, spatio-temporal joint clustering, spatio-temporal similarity measure, step-by-step merge strategy, hierarchical clustering, spatio-temporal scales, movement patterns, spatial linkage