Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization

Ci, Song; Pei, Tao; Ma, Ting; Du, Yunyan; Shu, Huan; Guo, Sihui; Fan, Zide

doi:10.1080/13658816.2018.1516287

Cited by 43 publications

(19 citation statements)

References 39 publications

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“…The second level of HSM M is for the sequence trajectories, which composes the basic spatial structure of complex trajectories. Each sequence trajectory consists of a start node and an end node, which is similar to Origin-Destination (OD) flow data (Zhu et al, 2018;Song et al, 2018). Based on the first level of HSM M , two sequence trajectories are more spatiotemporally similar if the spatial distance between their start nodes and their end nodes are smaller, and the periods during which they occur overlap more.…”

Section: Hierarchical Similarity Measurement Methodsmentioning

confidence: 99%

A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories

Liu

Xue

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Considering the critical role of trajectory data in Big Data era for dynamic geographical processes, human behaviour analysis and meteorological prediction, trajectory clustering has attracted growing attention. Many literatures have discussed the spatiot emporal clustering method of simple trajectories (i.e., has no branches, e.g. vehicle trajectories), yet there are few researches for clustering complex trajectories (i.e., has at least one split and/or merger and/or split -merger branch, e.g. ocean eddy trajectories, rainstorm trajectories). For addressing this issue, we propose a Process-Oriented Spatiotemporal Clustering Method (POSCM) for clustering complex trajectory data. The POSCM includes three parts: the first uses the semantic of process-sequence-state to represent the complex trajectories; the second proposes a Hierarchical Similarity Measurement Method (HSMM) to get the similarity between any two complex trajectories; in the last step, the complex trajectories clustering pattern is extracted through density-based clustering algorithm. Experiments on simulated trajectories are used to evaluate the POSCM and demonstrate the advantage by comparing against that of the VF2 algorithm. The POSCM is applied to the sea surface temperature abnormal variations trajectories from January 1950 to December 2017 in the Pacific Ocean. As shown in this case study, some new mined spatiotemporal patterns can provide new references for understanding the behaviours of marine abnormal variations under the background of the global change.</p>

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Section: Hierarchical Similarity Measurement Methodsmentioning

confidence: 99%

A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories

Liu

Xue

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…However, in the specific algorithm, the search radius based on OD points is adopted, which is dependent on the similarity of points to iterate. Some scholars proposed a spatial scan statistical method based on ant colony optimization to detect OD clusters of arbitrary shape [38]. The definition of OD clusters also depends on the internal connectivity of the OD points.…”

Section: Clustering Of Od Flowmentioning

confidence: 99%

“…In the research of spatial pattern recognition of OD flow clustering, there are three main methods to identify spatial distribution [38]. Among them, there are two main types of improved classical clustering algorithm, hierarchical clustering algorithm for OD flow and density clustering algorithm based on origin and destination points.…”

Section: Selection Of Basic Clustering Algorithmmentioning

confidence: 99%

An OD Flow Clustering Method Based on Vector Constraints: A Case Study for Beijing Taxi Origin-Destination Data

Guo

Zhang

et al. 2020

IJGI

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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.

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“…The analysis of the OD flows of taxi traces [11], mobile phones [12,13], and Integrated Circuit (IC) cards [14,15] can help to explore human mobility and location characteristics and provide necessary data for traffic planning, road construction, as well as traffic control and management [16,17]. An analysis of cycling flow between OD pairs is required for the spatial distribution and movements for dockless shared bicycles.Many studies have focused on finding clusters of origin and destination points by considering the attributes and spatial distributions of the OD points [18]. Among them, spatial statistics-based methods such as Moran's I [19], hierarchical clustering methods such as K-nearest-neighbors [20], and density-based clustering methods including Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [21] can be used for clustering origin and destination points.…”

mentioning

confidence: 99%

“…Many studies have focused on finding clusters of origin and destination points by considering the attributes and spatial distributions of the OD points [18]. Among them, spatial statistics-based methods such as Moran's I [19], hierarchical clustering methods such as K-nearest-neighbors [20], and density-based clustering methods including Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [21] can be used for clustering origin and destination points.…”

mentioning

confidence: 99%

Analysis of the Cycling Flow Between Origin and Destination for Dockless Shared Bicycles Based on Singular Value Decomposition

Cao

Cai

et al. 2019

IJGI

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Recently, an increasing number of cities have deployed bicycle-sharing systems to solve the first/last mile connection problem, generating a large quantity of data. In this paper, singular value decomposition (SVD) was used to extract the main features of the cycling flow from the origin and destination (OD) data of shared bicycles in Beijing. The results show that (1) pairs of OD flow clusters can be derived from the pairs of vectors after SVD, and each pair of clusters represents a small part of an area with dockless shared bicycles; (2) the spatial clusters derived from the top vectors of SVD are highly coincident with the hot spot areas in the heatmap of shared bicycles; (3) approximately 30% of the study area accounts for nearly 80% of bike riding; (4) nearly 70% of the clustered area derived from the top 1000 vectors of SVD is associated with subway stations; and (5) the types of point of interest (POI) differ between the origin area and destination area for the clustered area of the top 1000 vectors.However, there is limited research on the cycling flow between origin and destination (OD) pairs for dockless shared bicycles. Research on cycling flow could present the spatial distribution and movements of sharing bicycles on a smaller scale [10]. In this paper, the OD flow is defined as the cycling flow between a pair of OD points. The analysis of the OD flows of taxi traces [11], mobile phones [12,13], and Integrated Circuit (IC) cards [14,15] can help to explore human mobility and location characteristics and provide necessary data for traffic planning, road construction, as well as traffic control and management [16,17]. An analysis of cycling flow between OD pairs is required for the spatial distribution and movements for dockless shared bicycles.Many studies have focused on finding clusters of origin and destination points by considering the attributes and spatial distributions of the OD points [18]. Among them, spatial statistics-based methods such as Moran's I [19], hierarchical clustering methods such as K-nearest-neighbors [20], and density-based clustering methods including Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [21] can be used for clustering origin and destination points. Some studies have proposed many methods for extracting OD clusters using long-distance OD data, such as taxi trips. Bicycles are not suitable for long-distance commuting due to the restrictions of the bicycle itself. Some methods include, for example, the simple line clustering method [22], based on the OD line to find spatial linkage. However, this research attempts to construct an OD matrix to specify the travel demand from origin points to destination points microscopically from the perspective of short-distance commuting.Usually, an OD matrix is a large sparse matrix [23]. A dimensionality reduction method, which is called feature extraction, is necessary on such a large matrix to simplify the data without losing too much information [24]. Many methods are used to mine information from the OD ma...

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Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization

Cited by 43 publications

References 39 publications

A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories

A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories

An OD Flow Clustering Method Based on Vector Constraints: A Case Study for Beijing Taxi Origin-Destination Data

Analysis of the Cycling Flow Between Origin and Destination for Dockless Shared Bicycles Based on Singular Value Decomposition

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