Abstract
Origin-destination (OD) flow pattern mining is an important research method of urban dynamics, in which OD flow clustering analysis discovers the activity patterns of urban residents and mine the coupling relationship of urban subspace and dynamic causes. The existing flow clustering methods are limited by the spatial constraints of OD points, rely on the spatial similarity of geographical points, and lack in-depth analysis of high-dimensional flow characteristics, and therefore it is difficult to find irregular flow clusters. In this paper, we propose an OD flow clustering method based on vector constraints (ODFCVC), which defines OD flow event point and OD flow vector to express the spatial location relationship and geometric flow behavior characteristics of OD flow. First, the OD flow vector coordinate system is normalized by the Euclidean distance-based OD flow event point spatial clustering, and then the OD flow clusters with similar flow patterns are mined using adjusted cosine similarity-based OD flow vector feature clustering. The transformation of OD data from point set space to vector space is realized by constraining the vector coordinate system and vector similarity through two-step clustering, which simplifies the calculation of high-dimensional similarity of OD flow and helps mining representative OD flow clusters in flow space. Due to the OD flow cluster property, the k-means algorithm is selected as the basic clustering logic in the two-step clustering method, and a sum of squared error perceptually important points algorithm considering silhouette coefficients (SSEPIP) is adopted to automatically extract the optimal cluster number without defining any parameters. Tested by origin-destination flow data in Beijing, China, new traffic flow communities based on traffic hubs are obtained by using the ODFCVC method, and irregular traffic flow clusters (including cluster mode, divergence mode, and convergence mode) with representative travel trends are found.
Highlights
Origin-destination (OD) flow is the semantic recognition and feature extraction of complex trajectory data
Facing the problems of time delay and non-dynamics caused by the dependence of inherent geographic units in geographic flow pattern mining, this paper proposes an OD flow clustering method based on vector constraints
The pattern characteristics of OD flow are represented by OD flow event points and OD flow vector, and OD data is mapped from massive point set space to independent vector feature space
Summary
Origin-destination (OD) flow is the semantic recognition and feature extraction of complex trajectory data. According to the spatial characteristics of OD points, the point clustering algorithm is used to realize OD flow clustering through double-iteration [12,13,14,15] These OD flow clustering algorithms are constrained by the spatial distribution of OD points and the setting of search radius or internal connectivity parameters. They do not have the ability to discover irregular flow clusters actively. The existing clustering methods of geographic OD flow rely on the characteristics of geographical units and functional areas, and too closely link the inherent land use data of the origin and destination points with the dynamic geographic flow behavior. Taking Beijing taxi OD data as an example, this study uses the method to find taxi traffic flow communities and irregular shape clusters with the same flow pattern
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