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• W3209742710 abstract "Providing efficient human mobility services and infrastructure is one of the major concerns of most mid-sized to large cities around the world. A proper understanding of the dynamics of commuting flows is, therefore, a requisite to better plan urban areas. In this context, an important task is to study hypothetical scenarios in which possible future changes are evaluated. For instance, how the increase in residential units or transportation modes in a neighborhood will change the commuting flows to or from that region? In this paper, we propose to leverage GMEL, a recently introduced graph neural network model, to evaluate changes in commuting flows taking into account different land use and infrastructure scenarios. We validate the usefulness of our methodology through real-world case studies set in two large cities in Brazil." @default.
• W3209742710 created "2021-11-08" @default.
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• W3209742710 date "2021-10-25" @default.
• W3209742710 modified "2023-09-27" @default.
• W3209742710 title "Transportation Scenario Planning with Graph Neural Networks" @default.
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• W3209742710 hasPublicationYear "2021" @default.
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