Looking for Sustainable Urban Mobility through Bayesian Networks

There is no formalised theory of sustainable urban mobility systems. Observed patterns of urban mobility are often considered unsustainable. But we don’t know what a city with sustainable mobility should look like. It is nevertheless increasingly apparent that the urban mobility system plays an impo...

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Main Author: Giovanni Fusco
Format: Article
Language:deu
Published: Unité Mixte de Recherche 8504 Géographie-cités 2004-11-01
Series:Cybergeo
Subjects:
Online Access:http://journals.openedition.org/cybergeo/2777
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spelling doaj-b7e5bce4a3824f19a673cb02ea477a4d2021-10-05T13:15:55ZdeuUnité Mixte de Recherche 8504 Géographie-citésCybergeo1278-33662004-11-0110.4000/cybergeo.2777Looking for Sustainable Urban Mobility through Bayesian NetworksGiovanni FuscoThere is no formalised theory of sustainable urban mobility systems. Observed patterns of urban mobility are often considered unsustainable. But we don’t know what a city with sustainable mobility should look like. It is nevertheless increasingly apparent that the urban mobility system plays an important role in the achievement of the city’s wider sustainability objectives.In this paper we explore the characteristics of sustainable urban mobility systems through the technique of Bayesian networks. At the frontier between multivariate statistics and artificial intelligence, Bayesian networks provide powerful models of causal knowledge in an uncertain context. Using data on urban structure, transportation offer, mobility demand, resource consumption and environmental externalities from seventy-five world cities, we developed a systemic model of the city-transportation-environment interaction in the form of a Bayesian network. The network could then be used to infer the features of the city with sustainable mobility.The Bayesian model indicates that the city with sustainable mobility is most probably a dense city with highly efficient transit and multimodal mobility. It produces high levels of accessibility without relying on a fast road network. The achievement of sustainability objectives for urban mobility is probably compatible with all socioeconomic contexts.By measuring the distance of world cities from the inferred sustainability profile, we finally derive a geography of sustainability for mobility systems. The cities closest to the sustainability profile are in Central Europe as well as in affluent countries of the Far East. Car-dependent American cities are the farthest from the desired sustainability profile.http://journals.openedition.org/cybergeo/2777systemurban sustainabilityurban mobilitybayesian networksbayesian inferenceworld city
collection DOAJ
language deu
format Article
sources DOAJ
author Giovanni Fusco
spellingShingle Giovanni Fusco
Looking for Sustainable Urban Mobility through Bayesian Networks
Cybergeo
system
urban sustainability
urban mobility
bayesian networks
bayesian inference
world city
author_facet Giovanni Fusco
author_sort Giovanni Fusco
title Looking for Sustainable Urban Mobility through Bayesian Networks
title_short Looking for Sustainable Urban Mobility through Bayesian Networks
title_full Looking for Sustainable Urban Mobility through Bayesian Networks
title_fullStr Looking for Sustainable Urban Mobility through Bayesian Networks
title_full_unstemmed Looking for Sustainable Urban Mobility through Bayesian Networks
title_sort looking for sustainable urban mobility through bayesian networks
publisher Unité Mixte de Recherche 8504 Géographie-cités
series Cybergeo
issn 1278-3366
publishDate 2004-11-01
description There is no formalised theory of sustainable urban mobility systems. Observed patterns of urban mobility are often considered unsustainable. But we don’t know what a city with sustainable mobility should look like. It is nevertheless increasingly apparent that the urban mobility system plays an important role in the achievement of the city’s wider sustainability objectives.In this paper we explore the characteristics of sustainable urban mobility systems through the technique of Bayesian networks. At the frontier between multivariate statistics and artificial intelligence, Bayesian networks provide powerful models of causal knowledge in an uncertain context. Using data on urban structure, transportation offer, mobility demand, resource consumption and environmental externalities from seventy-five world cities, we developed a systemic model of the city-transportation-environment interaction in the form of a Bayesian network. The network could then be used to infer the features of the city with sustainable mobility.The Bayesian model indicates that the city with sustainable mobility is most probably a dense city with highly efficient transit and multimodal mobility. It produces high levels of accessibility without relying on a fast road network. The achievement of sustainability objectives for urban mobility is probably compatible with all socioeconomic contexts.By measuring the distance of world cities from the inferred sustainability profile, we finally derive a geography of sustainability for mobility systems. The cities closest to the sustainability profile are in Central Europe as well as in affluent countries of the Far East. Car-dependent American cities are the farthest from the desired sustainability profile.
topic system
urban sustainability
urban mobility
bayesian networks
bayesian inference
world city
url http://journals.openedition.org/cybergeo/2777
work_keys_str_mv AT giovannifusco lookingforsustainableurbanmobilitythroughbayesiannetworks
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