Data-driven micromobility network planning for demand and safety

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