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The road to direction: Assessing the impact of road asymmetry on street network small-worldness

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Small-world networks have proven to be optimal navigational structures, by insuring an adequate balance between local and global network efficiency. In the particular case of road networks, small-world- oriented research has led to widely diverging results, depending on modelling procedures: while traditional, geometric, road-based models fail to observe small-world properties in road networks, a new street-based modelling approach has obtained opposite results, by observing small-world properties for both named-based and angularity-based street graphs. These results are however hampered by the fact that street-based modelling has so far overlooked road asymmetry. Given this, the present research aims at evaluating the impact of road asymmetry on street network "small-worldness", by comparing symmetric and asymmetric street graphs by means of a structural indicator recently developed in brain network analysis. Results show that taking into account road asymmetry better highlights not only the small-world nature of street networks, but also the exceptional structure of name-based (odonymic) street topologies.

Original languageEnglish
Title of host publicationSpatial Cognition IX - International Conference, Spatial Cognition 2014, Proceedings
Number of pages16
PublisherSpringer-Verlag
Publication year1 Jan 2014
Pages206-221
ISBN (print)9783319112145
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
EventInternational Conference on Spatial Cognition IX, Spatial Cognition 2014 - Bremen, Germany
Duration: 15 Sep 201419 Sep 2014

Conference

ConferenceInternational Conference on Spatial Cognition IX, Spatial Cognition 2014
LandGermany
ByBremen
Periode15/09/201419/09/2014
SponsorSFB/TR 8 Spatial Cognition, Spatial Intelligence and Learning Center, Transregional Collaborative Research Center
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8684 LNAI
ISSN0302-9743

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