Clustering Daily Metro Origin-Destination Matrix in Shenzhen China

Yang, Chao; Yan, Fei; Xu, Xiang

doi:10.4028/www.scientific.net/amm.743.422

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Only a few studies have been conducted on the regularity of consecutive travel demand. Yang et al [29] used principal component analysis (PCA) and SVD along with the cluster method to conduct dimensionality reduction to the 290‐day's daily metro OD data of Shenzhen, China. They then used affinity propagation to cluster the dimensionality‐reduced OD matrix and identified 11 representative categories of demand patterns.…”

Section: Related Workmentioning

confidence: 99%

Understanding multiple days’ metro travel demand at aggregate level

Duan

Lei

Zhang

et al. 2019

IET Intelligent Transport Systems

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Section: Related Workmentioning

confidence: 99%

Understanding multiple days’ metro travel demand at aggregate level

Duan

Lei

Zhang

et al. 2019

IET Intelligent Transport Systems

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“…We explored the distribution of travel time classified according to the spatio-temporal characteristics of stable patterns. The proposed method can decompose stable travel patterns from the collective mobility and the results in this study can help us to better understand different mobility patterns in both spatial and temporal dimensions.Sustainability 2020, 12, 1475 2 of 16 analysis [5][6][7] in metro systems. The fast development of information technology has enabled researchers to obtain data reflecting travels through various means, such as GPS [8,9], mobile phones [10], and smart card systems [11][12][13][14].…”

mentioning

confidence: 99%

Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China

et al. 2020

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Individual mobility patterns are an important factor in urban traffic planning and traffic flow forecasting. How to understand the spatio-temporal distribution of passengers deeply and accurately, so as to provide theoretical support for the planning and operation of the metro network, is an urgent issue of wide concern. In this paper, we applied NCP decomposition to uncover the characteristics of travel patterns from temporal and spatial dimensions in the metro network of Shenzhen City. Utilizing matrix factorization and correlation analysis, we extracted several stable components from the collective mobility and find that the departure and arrival mobility patterns have different characteristics in both the temporal and spatial dimension. According to the point of interest (POI) data in the Shenzhen City, the function attributes of the station are identified and then we found that the spatial distribution characteristics of different patterns are different. We explored the distribution of travel time classified according to the spatio-temporal characteristics of stable patterns. The proposed method can decompose stable travel patterns from the collective mobility and the results in this study can help us to better understand different mobility patterns in both spatial and temporal dimensions.Sustainability 2020, 12, 1475 2 of 16 analysis [5][6][7] in metro systems. The fast development of information technology has enabled researchers to obtain data reflecting travels through various means, such as GPS [8,9], mobile phones [10], and smart card systems [11][12][13][14]. The emergence of large-scale data brings us new opportunities to better understand the characteristic of individual movement. Specifically, some scholars have explored human activity patterns by analyzing the spatial-temporal characteristic of travel [15][16][17][18][19]. Zhang et al. [15] applied a density-based method for identifying so-called temporal areas of interest (TAI), and found that there are four major types of TAIs on weekdays, namely work-like, morning, afternoon and nightlife TAIs, and three on weekends, namely work-like, day activity and nightlife TAIs. Using visualization techniques, Sun et al. [16] demonstrated the spatial and temporal distributions of passenger flows in a holistic manner, as well as the flow directional imbalances. Zhao et al.[17] used statistical-based and unsupervised clustering-based methods to understand the hidden regularities and anomalies of travel patterns and classify passengers in terms of the similarity of their travel patterns.Furthermore, the prediction of passengers' future trajectories [20][21][22][23][24] is also an important issue in transit systems. Yang et al. [20] argued that future movements of different types of groups can be predicted with high confidence based on previous records. Truong et al. [21] effectively predicted the inand outflow of passengers at a station over time based on the patterns of passenger flow with respect to time and stations. In the study [22], Shanghai was taken...

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“…These studies show that SVD can find the main components from matrix-shaped data. In particular, SVD has been used to extract features from the OD matrix of smart card records of Shenzhen, China [31]. Currently, little research exists on applying SVD directly to OD data mining and extraction.…”

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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Clustering Daily Metro Origin-Destination Matrix in Shenzhen China

Cited by 6 publications

References 12 publications

Understanding multiple days’ metro travel demand at aggregate level

Understanding multiple days’ metro travel demand at aggregate level

Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China

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

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