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<dc:title>Public transportation multimodality in the city of Lisbon</dc:title>
<dc:creator>Lemonde de Macedo, Carlos</dc:creator>
<dc:creator>Arsenio, Elisabete</dc:creator>
<dc:creator>Henriques, Rui</dc:creator>
<dc:subject>Planificación del transporte</dc:subject>
<dc:subject>Transporte público</dc:subject>
<dc:subject>Planning of transport</dc:subject>
<dc:subject>Public transport</dc:subject>
<dc:description>Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos</dc:description>
<dc:description>Mobility in major European capitals is not yet sustainable. The need to respond to the&#xd;
ongoing changes in public transportation demand, operationalize safety norms of social&#xd;
distancing, and reach carbon neutrality are prompting cities to reassess public transport&#xd;
systems. Cross-mode synergies in multimodal transport systems can be explored (including&#xd;
convenience, reliability, cost, speed and predictability) to foment public and active modes&#xd;
of mobility. In this context, multimodal traffic pattern analysis can unravel cross-mode&#xd;
vulnerabilities, a possibility that is finally rising with the sensorization of cities, integration&#xd;
of ticketing systems, and consolidation of traffic data sources and their situational context.&#xd;
This work introduces a methodology for the analysis of spatiotemporal indices of&#xd;
multimodality against available situational context, aiding specialists to find vulnerabilities&#xd;
on the public transportation network. Traffic generation poles, large-scale public events, and&#xd;
weather records are the considered sources of situational context. We discuss the role of&#xd;
context-aware multimodality indices to understand demand and its emerging changes, assess&#xd;
cross-modal transfers and preferences, and support cross-mode route and schedule planning.&#xd;
This work further discusses the relevance of multimodal pattern discovery to offer datacentric&#xd;
views ensuring: fully transparent decisions to the citizens; and an objective&#xd;
coordination between carriers, municipalities and authorities. Lisbon is further introduced in&#xd;
this work as a reference case study for context-aware multimodal mobility.</dc:description>
<dc:date>2022-09-21T10:21:58Z</dc:date>
<dc:date>2022-09-21T10:21:58Z</dc:date>
<dc:date>2021-07</dc:date>
<dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
<dc:identifier>978-84-18465-12-3</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/6978</dc:identifier>
<dc:identifier>10.36443/10259/6978</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>R-Evolucionando el transporte</dc:relation>
<dc:relation>http://hdl.handle.net/10259/6490</dc:relation>
<dc:relation>https://doi.org/10.36443/9788418465123</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/FCT//DSAIPA%2FDS%2F0111%2F2018</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/FCT//UIDB%2F50021%2F2020</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:publisher>Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional</dc:publisher>
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