  Developing safe infrastructure for micromobility like bicycles or e-scooters
is an efficient pathway towards climate-friendly, sustainable, and livable
cities. However, urban micromobility infrastructure is typically planned ad-hoc
and at best informed by survey data. Here we study how data of micromobility
trips and crashes can shape and automatize such network planning processes. We
introduce a parameter that tunes the focus between demand-based and
safety-based development, and investigate systematically this tradeoff for the
city of Turin. We find that a full focus on demand or safety generates
different network extensions in the short term, with an optimal tradeoff
in-between. In the long term our framework improves overall network quality
independent of short-term focus. Thus, we show how a data-driven process can
provide urban planners with automated assistance for variable short-term
scenario planning while maintaining the long-term goal of a sustainable,
city-spanning micromobility network.
