Global sustainability requires low-carbon urban transport systems, shaped by
adequate infrastructure, deployment of low-carbon transport modes and shifts in
travel behavior. To adequately implement alterations in infrastructure, it's
essential to grasp the location-specific cause-and-effect mechanisms that the
constructed 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 modeling urban form effects
at high spatial resolution. Here, we address all three gaps by utilizing a
causal discovery and an explainable machine learning framework to detect urban
form effects on intra-city travel based on high-resolution mobility data of six
cities across three continents. We show that both distance to city center,
demographics and density indirectly affect other urban form features. By
considering the causal relationships, we find 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. Differences in urban form effects across the cities call
for a more holistic definition of 6D measures. Our work is a starting point for
location-specific analysis of urban form effects on mobility behavior using
causal discovery approaches, which is highly relevant for city planners and
municipalities across continents.Comment: 22 pages, 13 figures, 4 table