35 citations · 35 across the 2 of their papers we have counts for
6 papers
Ride-pooling adoption model for emission estimation
Milli Keil, Felix Creutzig, Nora Molkenthin
With the climate emergency and growing challenges ranging from pollution to congestion, ride-pooling (rp) has been floated as a potential solution for less congested, low-carbon an…
The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection
Florian Nachtigall, Felix Wagner, Peter Berrill +1
Understanding why travel behavior differs between residents of urban centers and suburbs is key to sustainable urban planning. Especially in light of rapid urban growth, identifyin…
Shared Mobility in Berlin: An Analysis of Ride-Pooling with Car Mobility Data
Alexander Schmaus, Felix Creutzig, Nicolas Koch +1
In face of the threat of a climate catastrophe and the resulting urgent need for decarbonization together with the widespread emergence of the sharing economy, shared pooled mobili…
Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents
Felix Wagner, Florian Nachtigall, Lukas Franken +6
Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current poli…
Open government geospatial data on buildings for planning sustainable and resilient cities
Filip Biljecki, Lawrence Zheng Xiong Chew, Nikola Milojevic-Dupont +1
As buildings are central to the social and environmental sustainability of human settlements, high-quality geospatial data are necessary to support their management and planning. A…
Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack +19
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a po…