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http://repositorio.lnec.pt:8080/jspui/handle/123456789/1015609
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DC Field | Value | Language |
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dc.contributor.author | Aparício, J. | pt_BR |
dc.contributor.author | Arsénio, E. | pt_BR |
dc.contributor.author | Santos, F. | pt_BR |
dc.contributor.author | Henriques, R. | pt_BR |
dc.date.accessioned | 2022-12-14T11:50:35Z | pt_BR |
dc.date.accessioned | 2023-03-03T10:56:56Z | - |
dc.date.available | 2022-12-14T11:50:35Z | pt_BR |
dc.date.available | 2023-03-03T10:56:56Z | - |
dc.date.issued | 2022-06-21 | pt_BR |
dc.identifier.citation | https://doi.org/ 10.3390/su14137891 | pt_BR |
dc.identifier.uri | https://repositorio.lnec.pt/jspui/handle/123456789/1015609 | - |
dc.description.abstract | This study aims to contribute to more sustainable mobility solutions by proposing robust and actionable methods to assess the resilience of a multimodal transport system. Resilience is seen in a dynamic lean setting, looking at aspects in the network topology and user’s flow and demand throughout a parameterizable period. We hypothesize that this network’s appropriate multi-layered and traffic-sensitive modeling can promote the integrated analysis of different transport modes and support an improved resilience analysis. We operationalize the lean resilience conceptual construct with the proposed muLtImodal traNsportation rEsilience aSsessment (LINES) methodological process. Using the city of Lisbon as a study case, we illustrate the relevance of the proposed methodology to detect actionable vulnerabilities in the bus–tram–subway network. | pt_BR |
dc.language.iso | eng | pt_BR |
dc.publisher | MDPI | pt_BR |
dc.relation | DSAIPA/DS/0111/2018 FCT | pt_BR |
dc.rights | openAccess | pt_BR |
dc.subject | Multimodality | pt_BR |
dc.subject | Resilience | pt_BR |
dc.subject | Sustainable mobility | pt_BR |
dc.subject | Big data | pt_BR |
dc.title | LINES: muLtImodal traNsportation rEsilience analySis | pt_BR |
dc.type | article | pt_BR |
dc.description.pages | 20p | pt_BR |
dc.description.comments | Estudo elaborado no âmbito do projeto ILU -“Integrative Learning from Urban Data and Situational Context for City Mobility Optimization”/ Aprendizagem avançada em dados urbanos com conteúdo situacional para otimização da mobilidade nas cidades, financiado pela Fundação para a Ciência e a Tecnologia (FCT) entre janeiro de 2019 e 30 de junho de 2022. O estudo contou com o apoio da Câmara Municipal de Lisboa e das empresas CARRIS e Metro. | pt_BR |
dc.description.volume | 14 | pt_BR |
dc.description.sector | DT/CHEFIA | pt_BR |
dc.identifier.proc | 0701/1101/2160201 | pt_BR |
dc.description.magazine | Sustainability | pt_BR |
dc.contributor.peer-reviewed | SIM | pt_BR |
dc.contributor.academicresearchers | SIM | pt_BR |
dc.contributor.arquivo | SIM | pt_BR |
Appears in Collections: | DT/Chefia - Comunicações a congressos e artigos de revista |
Files in This Item:
File | Description | Size | Format | |
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Sustainability_Resumo.pdf | Resumo | 337.46 kB | Adobe PDF | View/Open |
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