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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cerqueira, S. | pt_BR |
dc.contributor.author | Arsénio, E. | pt_BR |
dc.contributor.author | Henriques, R. | pt_BR |
dc.contributor.author | Barateiro, J. | pt_BR |
dc.date.accessioned | 2025-02-21T15:43:49Z | pt_BR |
dc.date.accessioned | 2025-04-16T13:40:00Z | - |
dc.date.available | 2025-02-21T15:43:49Z | pt_BR |
dc.date.available | 2025-04-16T13:40:00Z | - |
dc.date.issued | 2024-04-15 | pt_BR |
dc.identifier.citation | Cerqueira, S., Arsénio, E., Henriques, R., Barateiro, J. (2024) Freight demand modeling for green and digital logistics, 10th Transport Research Arena (TRA) Conference, Dublin. | pt_BR |
dc.identifier.uri | http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018387 | - |
dc.description.abstract | Freight transport demand modelling (FTDM) from dispatch logs and partial observations play a key role in providing realistic expectations of freight transport needs and in assessing the environmental impacts of new policies. Shaping freight transport to answer the current challenges of green logistics – decarbonization, energy consumption optimization, intelligent and sustainable management – requires a continuous assessment and adaptation of the whole logistic system. Demand-centric models may support these ends. Understanding advances in freight modelling is thus crucial to overcoming FTDM challenges, including data scarcity. This paper provides two major contributions: i) a review of the state-of-the-art models on freight transport demand modelling, and ii) an integrated overview of how these models can support decision-making aligned with sustainability goals towards green (zero emissions) and digital logistics. | pt_BR |
dc.language.iso | eng | pt_BR |
dc.publisher | Springer | pt_BR |
dc.relation | Horizon Europe | pt_BR |
dc.rights | restrictedAccess | pt_BR |
dc.subject | Freight demand modelling | pt_BR |
dc.subject | Green logistics | pt_BR |
dc.subject | Digital logistics | pt_BR |
dc.subject | Freight origin-destination models | pt_BR |
dc.subject | Zero emissons | pt_BR |
dc.subject | Sustainable transport and logistics | pt_BR |
dc.subject | Artificial intelligence | pt_BR |
dc.subject | Intelligent transport and supply chains | pt_BR |
dc.title | Freight demand modeling for green and digital logistics | pt_BR |
dc.type | workingPaper | pt_BR |
dc.description.pages | 6p. | pt_BR |
dc.description.comments | Estudo realizado no âmbito do projeto de I&I ADMIRAL financiado pelo programa Horizonte Europa (Grant Agreement 101104163), coordenado no LNEC pela IP Elisabete Arsénio. A comunicação foi aceite para publicação pela Springer. | pt_BR |
dc.description.sector | DT/CHEFIA | pt_BR |
dc.identifier.proc | 0701/1101/23484 | pt_BR |
dc.identifier.conftitle | 10th Transport Research Arena (TRA) Conference 2024 / TRA 2024 | pt_BR |
dc.contributor.peer-reviewed | NAO | 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 |
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