Data-driven micromobility network planning for demand and safety

Pietro Folco, Laetitia Gauvin, Michele Tizzoni, Michael Szell

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review


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.
Original languageEnglish
Article number0
JournalEnvironment and Planning B
Issue number0
Pages (from-to)1-16
Number of pages16
Publication statusPublished - 22 Oct 2022


  • urban data science
  • micromobility infrastructure
  • sustainable mobility
  • road safety


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