Extracting the Multimodal Fingerprint of Urban Transportation Networks

Luis Guillermo Natera Orozco; Federico Battiston; Gerardo Iñiguez; Michael Szell

doi:10.32866/001c.13171

Extracting the Multimodal Fingerprint of Urban Transportation Networks

Luis Guillermo Natera Orozco, Federico Battiston, Gerardo Iñiguez, Michael Szell^*

^*Corresponding author for this work

Department of Network and Data Science

Research output: Contribution to journal › Article › peer-review

Abstract (may include machine translation)

Urban mobility increasingly relies on multimodality, combining the use of bicycle paths, streets, and rail networks. These different modes of transportation are well described by multiplex networks. Here we propose the overlap census method which extracts a multimodal profile from a city’s multiplex transportation network. We apply this method to 15 cities, identify clusters of cities with similar profiles, and link this feature to the level of sustainable mobility of each cluster. Our work highlights the importance of evaluating all the transportation systems of a city together to adequately identify and compare its potential for sustainable, multimodal mobility.

Original language	English
Journal	Transport Findings
Volume	2020
DOIs	https://doi.org/10.32866/001c.13171
State	Published - 10 Jun 2020

Keywords

multi-modal transportation
multiplex networks
sustainable transport

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.32866/001c.13171License: CC BY-SA

Battiston-Federico5_2020Publisher version, 2.25 MBLicense: CC BY

Cite this

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AU - Natera Orozco, Luis Guillermo

AU - Battiston, Federico

AU - Iñiguez, Gerardo

AU - Szell, Michael

PY - 2020/6/10

Y1 - 2020/6/10

N2 - Urban mobility increasingly relies on multimodality, combining the use of bicycle paths, streets, and rail networks. These different modes of transportation are well described by multiplex networks. Here we propose the overlap census method which extracts a multimodal profile from a city’s multiplex transportation network. We apply this method to 15 cities, identify clusters of cities with similar profiles, and link this feature to the level of sustainable mobility of each cluster. Our work highlights the importance of evaluating all the transportation systems of a city together to adequately identify and compare its potential for sustainable, multimodal mobility.

AB - Urban mobility increasingly relies on multimodality, combining the use of bicycle paths, streets, and rail networks. These different modes of transportation are well described by multiplex networks. Here we propose the overlap census method which extracts a multimodal profile from a city’s multiplex transportation network. We apply this method to 15 cities, identify clusters of cities with similar profiles, and link this feature to the level of sustainable mobility of each cluster. Our work highlights the importance of evaluating all the transportation systems of a city together to adequately identify and compare its potential for sustainable, multimodal mobility.

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KW - multiplex networks

KW - sustainable transport

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VL - 2020

JO - Transport Findings

JF - Transport Findings

ER -

Extracting the Multimodal Fingerprint of Urban Transportation Networks

Abstract (may include machine translation)

Keywords

UN SDGs

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Extracting the multimodal fingerprint of urban transportation networks

Cite this