Data-driven micromobility network planning for demand and safety

Pietro Folco, Laetitia Gauvin, Michele Tizzoni and Michael Szell

Environment and Planning B, 2023, vol. 50, issue 8, 2087-2102

Abstract: 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.

Keywords: urban data science; micromobility infrastructure; sustainable mobility; road safety (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:50:y:2023:i:8:p:2087-2102

DOI: 10.1177/23998083221135611

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