Multi-view metro station clustering based on passenger flows: a functional data-edged network community detection approach

Zhang, Chen; Zheng, Baihua; Tsung, Fugee

doi:10.1007/s10618-023-00916-w

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…Furthermore, scholars have employed various quantitative algorithms to conduct cluster analysis on the passenger flow of urban rail transit. Zhang (2023) developed a novel community detection algorithm based on nonnegative matrix tri-factorization for a higher granularity analysis of subway station passenger flow, enabling more flexible clustering analysis based on different passenger travel patterns [5]. Pang (2023) utilized smart card data to extract the dynamic characteristics of subway passenger flow and identified different patterns of subway station flow through hierarchical clustering and k-means clustering methods [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Urban rail transit plays a crucial role in urban economic growth and people's economic activities [4]. Urban rail transit is an important component of public transportation in large cities, and its stations, the nodes in the rail transit network [5], play the part of connectors between various transportation modes for catering to travel demands in different times and spaces [4]. It is widely recognized that there are substantial disparities in passenger flow among different rail transit stations and lines.…”

Section: Introductionmentioning

confidence: 99%

“…It has a significant effect on population agglomeration and spatial structure of cities [13] because of the distinct agglomeration patterns of land use types around different rail transit stations [4]. Therefore, analyzing the distribution of passenger flow and the agglomeration status of land types within rail transit station areas is of great importance for public transportation management and land planning in cities [5]. From the concept of traffic location, traffic location is the gathering place of traffic behavior and traffic resources [14].…”

Section: Introductionmentioning

confidence: 99%

“…This study aims to improve the efficiency of urban rail transit stations and lines and optimize the spatial layout of the urban rail transit network. It is important to study the heterogeneity of taxi passenger flow patterns and land agglomeration types around urban rail transit stations in different locations, as well as the interactions between them [16,5]. This study focuses on the following questions: 1.…”

Section: Introductionmentioning

confidence: 99%

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Clustering of Passenger Flow and Land-Use of Beijing Urban Rail Transit Stations Based on Multi-Source Data

2024

Teh. vjesn.

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With the deepening of transit-oriented development (TOD), the construction of high-density, multi-functional urban spatial structures around public transportation stations has become a significant focus in urban development planning. This study is aimed to explore the spatial and temporal cluster patterns and the relationship between taxi passenger flow and land use types around urban rail transit stations. Taking Beijing as an example, this study extracts the time series of pick-up and drop-off points from the taxi GPS track dataset and employs the affinity propagation (AP) method to conduct the spatial and temporal cluster analysis on these taxi pick-up and drop-off points. Then, based on the urban POI dataset, this study classifies these stations into four functional types by adopting the K-means method. Finally, the partial least squares (PLS) method has been used to explore the relationship between the taxi passenger flow and the land use types under different time intervals. The result indicates that: first, there is a regional aggregation effect for the taxi passenger source in the rail transit station area, and the cluster areas are mainly located in Chaoyang and Haidian districts, as well as along the circular metro line. Besides, there is a significant rush hour in the morning and evening for this passenger flow. Second, the commercial, residential, and comprehensive transit stations are mainly located in the central urban districts of Beijing with balanced spatial distribution, while the industrial transit stations are dispersed to the outer suburbs of the city, which conform to Alonso's spatial distribution of land. Third, there is a significant positive correlation between taxi passenger sources in the rail transit station area and land use types containing transportation service, public facility and daily life service. This study analyses the population mobility and land type cluster form around the urban rail transit from the micro-perspective, which verifies and supplements Alonso's transportation location theory. Therefore, this study contributes to the rational planning of urban spatial resource allocation and the construction of the urban rail transit network.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clustering of Passenger Flow and Land-Use of Beijing Urban Rail Transit Stations Based on Multi-Source Data

2024

Teh. vjesn.

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“…As a typical unsupervised data fusion and learning method, multi-view clustering (MVC) achieves semantic-level data fusion by leveraging the consistency and complementarity among multiple views, while obtaining the comprehensive latent data distribution [5][6][7][8][9][10][11]. Many advanced MVC methods have been proposed and some practical cases demonstrate their effectiveness [12][13][14][15]. For the elevator security warning task, the status data collected from different sources is regarded as the feature description of different views, called multi-view elevator status data.…”

Section: Introductionmentioning

confidence: 99%

Tensorial multi-view subspace clustering with side constraints for elevator security warning

Xu,

Ruan,

et al. 2024

Multimedia Systems

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In recent years, using clustering technology to realize equipment security warning is a research hotspot in the field of data mining applications. However, due to the lack of data fusion mechanism and prior knowledge guidance, the performance of most existing methods is limited when applied to complex equipment such as elevator. In this paper, a novel Tensorial Multi-view Subspace Clustering with Side constraints (TMSCS) is proposed for elevator security warning, which first introduces tensorial multi-view subspace learning to achieve data fusion based on high-order correlation. Secondly, the prior knowledge is formalized as side constraints between samples through adaptive graph learning, where certain elevators is forced to have similar or dissimilar operation status. Thirdly, a unified model combining tensorial multi-view subspace learning and adaptive graph learning is constructed to eliminate the instability caused by phased learning. Furthermore, an efficient optimization algorithm is designed to solve this model. Extensive experiments on several benchmark datasets demonstrate the superiority of our method, and the experimental results on real elevator status datasets demonstrate that our method accurately identifies the operating status of each elevator equipment.

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Understanding the intra-day and intra-week ridership patterns of urban rail transit stations in London using a fuzzy clustering approach

Cheng,

Hatzichristos,

Kostellou

et al. 2024

Journal of Public Transportation

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Multi-view metro station clustering based on passenger flows: a functional data-edged network community detection approach

Cited by 7 publications

References 31 publications

Clustering of Passenger Flow and Land-Use of Beijing Urban Rail Transit Stations Based on Multi-Source Data

Clustering of Passenger Flow and Land-Use of Beijing Urban Rail Transit Stations Based on Multi-Source Data

Tensorial multi-view subspace clustering with side constraints for elevator security warning

Understanding the intra-day and intra-week ridership patterns of urban rail transit stations in London using a fuzzy clustering approach

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