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Machine learning for spatial disaggregation of regional transport data in the EU

Fernandez, Juan R.
Type
Tesis de maestría
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Abstract
This thesis develops a machine learning framework for the spatial disaggregation of transport-related data across European Union regions. Using a self-supervised hybrid regression approach combined with dasymetric mapping and ancillary geospatial data, the study improves estimation accuracy at NUTS-3 level. Results demonstrate the potential of data-driven methods to support regional decarbonization strategies.
Description
Tesis Energía y Ambiente (maestría) - Instituto Tecnológico de Buenos Aires, Buenos Aires - Karlsruher Institut für Technologie, Karlsruhe, 2023
Date
2023-06-23
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Energía y Ambiente (ITBA-KIT)
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2023 Fernández, Juan Rodrigo - ITBA-KIT Master Thesis.pdf
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MACHINE LEARNING, SPATIAL DISAGGREGATION, TRANSPORT DATA, EUROPEAN UNION, SELF-SUPERVISED LEARNING, HYBRID REGRESSION, DASYMETRIC MAPPING, GEOSPATIAL ANALYSIS, APRENDIZAJE AUTOMÁTICO, DESAGREGACIÓN ESPACIAL, DATOS DE TRANSPORTE, UNIÓN EUROPEA, APRENDIZAJE AUTO-SUPERVISADO, REGRESIÓN HÍBRIDA, MAPEADO DASIMÉTRICO, ANÁLISIS GEOESPACIAL
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URI
https://hdl.handle.net/20.500.14769/5275
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