A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents (Papers Track)

Felix Wagner (TU Berlin, MCC Berlin); Florian Nachtigall (MCC Berlin); Lukas B Franken (University of Edinburgh); Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change (MCC)); Marta C. González (Berkeley); Jakob Runge (TU Berlin); Rafael Pereira (IPEA); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))

Poster File NeurIPS 2023 Poster Cite
Cities & Urban Planning Causal & Bayesian Methods


For low carbon transport planning it's essential to grasp the location-specific cause-and-effect mechanisms that the built environment has on travel. Yet, current research falls short in representing causal relationships between the "6D" urban form variables and travel, generalizing across different regions, and modelling urban form effects at high spatial resolution. Here, we address these gaps by utilizing a causal discovery and an explainable machine learning framework to detect urban form effects on intra-city travel emissions based on high-resolution mobility data of six cities across three continents. We show that distance to center, demographics and density indirectly affect other urban form features and that location-specific influences align across cities, yet vary in magnitude. In addition, the spread of the city and the coverage of jobs across the city are the strongest determinants of travel-related emissions, highlighting the benefits of compact development and associated benefits. Our work is a starting point for location-specific analysis of urban form effects on mobility using causal discovery approaches, which is highly relevant municipalities across continents.