Using explainable machine learning to understand how urban form shapes 
sustainable mobility

Wagner, Felix; Milojevic-Dupont, Nikola; Franken, Lukas; Zekar, Aicha; Thies, Ben; Koch, Nicolas; Creutzig, Felix

doi:10.1016/j.trd.2022.103442

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Using explainable machine learning to understand how urban form shapes sustainable mobility

Wagner, F., Milojevic-Dupont, N., Franken, L., Zekar, A., Thies, B., Koch, N., Creutzig, F. (2022): Using explainable machine learning to understand how urban form shapes sustainable mobility. - Transportation Research Part D: Transport and Environment, 111, 103442.
https://doi.org/10.1016/j.trd.2022.103442

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_31193 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_31193_1

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Creators:
Wagner, Felix¹, Author
Milojevic-Dupont, Nikola, Author
Franken, Lukas, Author
Zekar, Aicha, Author
Thies, Ben, Author
Koch, Nicolas, Author
Creutzig, Felix, Author

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1External Organizations, ou_persistent22

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Dates: Finally published : 2022

Publication Status: Finally published

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Identifiers: DOI: 10.1016/j.trd.2022.103442
BibTex Citekey: RN1275

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Title: Transportation Research Part D: Transport and Environment

Source Genre: Journal

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Pages: - Volume / Issue: 111 Sequence Number: - Start / End Page: 103442 Identifier: PIKDOMAIN: RD5 - Climate Economics and Policy - MCC Berlin
Organisational keyword: RD5 - Climate Economics and Policy - MCC Berlin
Working Group: Cities: Data Science and Sustainable Planning