Complex transportation networks: resilience, modelling and optimization

In this study, we have performed a comprehensive analysis of public transport networks(PTN) combining tools of complex network theory, computer modelling, and analytical calculations. We have started from an empirical analysis of the PTN of 14 major cities of the world and have determined their principal characteristics in terms of the complex network theory. Our empirical analysis gives a strong evidence, that the networks under consideration appear to be strongly correlated small-world structures with high values of clustering coefficients and comparatively low mean shortest path values. We further have introduced several PTN models. This was done both in 2d and 1d embedding spaces. In particular, our model of mutually interacting self-avoiding walks in 2d captures both special features of PTN as well as generating profiles of network characteristics in the various representations. We continued our analysis by studying the behavior of PTN under attacks. This enabled us to propose criteria that allow an a priori estimate of PTN robustness.

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Source https://theses.hal.science/tel-01746249
Author Holovatch, Taras
Maintainer CCSD
Last Updated November 10, 2026, 13:07 (UTC)
Created November 10, 2026, 13:07 (UTC)
Identifier NNT: 2011NAN10090
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Groupe de Physique statistique = Statistical Physics Group [Institut Jean Lamour] (Equipe 106, IJL) ; Institut Jean Lamour (IJL) ; Institut de Chimie - CNRS Chimie (INC-CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie - CNRS Chimie (INC-CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
creator Holovatch, Taras
date 2011-10-19T00:00:00
harvest_object_id 91ef98b4-3f95-4928-b574-036ca4762adc
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-11-04T00:00:00
set_spec type:THESE