Paper Info
Title
Orientations and matrix function-based centralities in multiplex network analysis of urban public transport
Abstract
We study urban public transport systems by means of multiplex networks in which stops are represented as nodes and each line is represented by a layer. We determine and visualize public transport network orientations and compare them with street network orientations of the 36 largest German as well as 18 selected major European cities. We find that German urban public transport networks are mainly oriented in a direction close to the cardinal east-west axis, which usually coincides with one of two orthogonal preferential directions of the corresponding street network. While this behavior is present in only a subset of the considered European cities it remains true that none but one considered public transport network has a distinct north-south-like preferential orientation. Furthermore, we study the applicability of the class of matrix function-based centrality measures, which has recently been generalized from single-layer networks to layer-coupled multiplex networks, to our more general urban multiplex framework. Numerical experiments based on highly efficient and scalable methods from numerical linear algebra show promising results, which are in line with previous studies. The centrality measures allow detailed insights into geometrical properties of urban systems such as the spatial distribution of major transport axes, which can not be inferred from orientation plots. We comment on advantages over existing methodology, elaborate on the comparison of different measures and weight models, and present detailed hyper-parameter studies. All results are illustrated by demonstrative graphical representations.
Year
DOI
Venue
2021
10.1007/s41109-021-00429-9
APPLIED NETWORK SCIENCE
Keywords
DocType
Volume
Multiplex networks, Urban systems, Public transport, Network orientation, Matrix function-based centralities
Journal
6
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Kai Bergermann100.34
Martin Stoll200.68