Combinatorial connectivity and spectral graph analytics for urban public transportation system

Boulmakoul, Badredine; Besri, Zineb; Karim, Lamia; Boulmakoul, Azedine; Lbath, Ahmed

doi:10.1016/j.trpro.2017.12.019

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…When creating such a system, it is necessary to use elements of discrete mathematicsthe theory of graphs. Elements of the theory of graphs can be used to determine alpha, beta and gamma indices of the network of high-speed passenger transport, which characterize the degree of transport system connectivity [12].…”

Section: Discussionmentioning

confidence: 99%

“…The object of research in this article is the passenger transport system of Moscow agglomeration, so the most interesting problem to study is the growth of labor push-pull migration. An important component of creating a sustainable public transport network of the agglomeration is the development of a system of passenger hubs [11], [12], [13], [14]. When elaborating a passenger hubs development plan, the top-priority problem is to satisfy needs for access for passengers and vehicles with their unique characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Development of Moscow region passenger transport system using discrete mathematics methods

Vlasov

Rasov

2020

E3S Web Conf.

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Transformation of settlement systems, consolidation of existing cities to the level of agglomerations, conurbations and megalopolises require a qualitative change in the transport system. Today Moscow and the Moscow region are in the stage of active development and interaction. The most important issue is to ensure transport connectivity and territory accessibility. The high cost of infrastructure projects and their duration require high-quality justification of decisions to be made. This publication discusses the use of the theory of graphs in the development of Light-Rail-Transit (LRT) systems, which are planned to be used as an element of the new unified transport system of Moscow agglomeration. The materials for the article are data on the existing and planned development of Moscow region settlements, the basis for calculations is the mathematical apparatus of the theory of graphs. The result of the work is represented by recommendations on the use of the theory of graphs for the purposes of substantiation of Moscow region transport system development, as well as main areas for continuing research were determined.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Moscow region passenger transport system using discrete mathematics methods

Vlasov

Rasov

2020

E3S Web Conf.

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“…The proposed neural network model implements a first order spatial hypergraph filter given by Y = σ (I + αΘ) XW (7) where α is a learnable graph filter coefficient, W is a learnable weight matrix performing feature extraction, and σ is an activation function. In a classical graph neural network implementation, we simply replace the hypergraph matrix, Θ, with the standard adjacency matrix, A [10].…”

Section: A Hypergraph Model Of Metro Networkmentioning

confidence: 99%

“…Graph representations for traffic planning have a long history [3]- [7]; as far back as in 1926, a map-maker named Fred Stingemore produced a map of the London underground by regularising the spacing between stations, while allowing himself some artistic freedom with the routes of the various lines. Stingemore's work was further abstracted by Harry Beck in 1933 to the graph form we have today.…”

Section: Introductionmentioning

confidence: 99%

Graph Theory for Metro Traffic Modelling

Dees¹,

Xu²,

Constantinides³

et al. 2021

Preprint

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A unifying graph theoretic framework for the modelling of metro transportation networks is proposed. This is achieved by first introducing a basic graph framework for the modelling of the London underground system from a diffusion law point of view. This forms a basis for the analysis of both station importance and their vulnerability, whereby the concept of graph vertex centrality plays a key role. We next explore k-edge augmentation of a graph topology, and illustrate its usefulness both for improving the network robustness and as a planning tool. Upon establishing the graph theoretic attributes of the underlying graph topology, we proceed to introduce models for processing data on such a metro graph. Commuter movement is shown to obey the Fick's law of diffusion, where the graph Laplacian provides an analytical model for the diffusion process of commuter population dynamics. Finally, we also explore the application of modern deep learning models, such as graph neural networks and hyper-graph neural networks, as general purpose models for the modelling and forecasting of underground data, especially in the context of the morning and evening rush hours. Comprehensive simulations including the passenger in-and outflows during the morning rush hour in London demonstrates the advantages of the graph models in metro planning and traffic management, a formal mathematical approach with wide economic implications.

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“…Within urban studies, spectral analysis has been used to determine points of centrality in an urban network using the eigenvectors as an alternative to geodesic centrality (Nourian et al 2016;Boulmakoul et al 2017). In another case, spectral analysis was used to compare global cities, but this work did not depend on geographical urban networks or spatial data, but rather a high-dimensional data set that described features of the various cities (Hanna 2009).…”

Section: Introductionmentioning

confidence: 99%

Spectral Clustering for Urban Networks

Patt¹

2020

CAADRIA Proceedings

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Combinatorial connectivity and spectral graph analytics for urban public transportation system

Cited by 4 publications

References 22 publications

Development of Moscow region passenger transport system using discrete mathematics methods

Development of Moscow region passenger transport system using discrete mathematics methods

Graph Theory for Metro Traffic Modelling

Spectral Clustering for Urban Networks

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