Identification of communities in urban mobility networks using multi-layer graphs of network traffic

Yildirimoḡlu, Mehmet; Kim, Jiwon

doi:10.1016/j.trc.2018.02.015

Cited by 74 publications

(28 citation statements)

References 34 publications

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“…The division of traffic zones (communities) is an important component of traffic surveys, travel demand forecasting, trip generation, and trip distribution [62]. The traditional traffic zone division method cannot reflect the latest or real-time traffic patterns and the consistent characteristics within a traffic zone and ignores the mobility and community characteristics of traffic behavior [62,63]. Our research applies the ODFCVC method to traffic OD flow, which can identify traffic flow communities with frequent internal interactions and regional interaction behaviors with typical travel patterns.…”

Section: Conclusion and Discussionmentioning

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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Section: Conclusion and Discussionmentioning

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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“…[43] use centrality measures for temporal prediction on OD networks built from cellular traffic data. [24], study temporal OD networks with change detection techniques for identifying "change points" in time, in which the entire structure of the graph changes.…”

Section: Plos Onementioning

confidence: 99%

“…Although some recent work has focused on analysing the spatial patterns of different urban features [5,21], studying urban networks with centrality measures [17,22,23], as well as modelling the evolution of urban interaction networks over time [24], we still have a poor understanding of the interplay between urban location characteristics and the networks of interactions between these locations. All the more so, the temporal evolution of this interplay remains an unexplored area of research.…”

Section: Introductionmentioning

confidence: 99%

Ranking places in attributed temporal urban mobility networks

et al. 2020

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Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socioeconomic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socioeconomic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socioeconomic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.

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“…It is desirable to represent high-level urban mobility structures with multi-scale communities when dealing with overwhelming amounts of mobility data [38]. Each mobility community is featured with similar travel characteristics.…”

Section: Discovering Mobility Communitiesmentioning

confidence: 99%

Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

Wang

Cui

et al. 2019

IJGI

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Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

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Identification of communities in urban mobility networks using multi-layer graphs of network traffic

Cited by 74 publications

References 34 publications

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

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

Ranking places in attributed temporal urban mobility networks

Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

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