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

Fernandez, Juan R · RI ITBA · 2026

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Fernandez, Juan R

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RI ITBA

Year

2026

Language

English

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Tesis Energía y Ambiente (maestría) - Instituto Tecnológico de Buenos Aires, Buenos Aires - Karlsruher Institut für Technologie, Karlsruhe, 2023 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.

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APA 7

Fernandez, J. R. (2026). Machine learning for spatial disaggregation of regional transport data in the EU. RI ITBA. https://hdl.handle.net/20.500.14769/5275

MLA

Fernandez, Juan R. Machine learning for spatial disaggregation of regional transport data in the EU. RI ITBA, 2026. https://hdl.handle.net/20.500.14769/5275.

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Fernandez, Juan R. 2026. Machine learning for spatial disaggregation of regional transport data in the EU. RI ITBA. https://hdl.handle.net/20.500.14769/5275.

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Fernandez, J. R. 2026, Machine learning for spatial disaggregation of regional transport data in the EU, RI ITBA, available at: https://hdl.handle.net/20.500.14769/5275 [Accessed 5 Aug. 2026].

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

Author / contributors: Fernandez, Juan R

Publisher: RI ITBA

Publication year: 2026

Language: English

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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