Data-driven micromobility network planning for demand and safety

• URL: http://arxiv.org/abs/2203.14619v2
• Date: Mon, 10 Oct 2022 11:29:13 GMT
• Title: Data-driven micromobility network planning for demand and safety
• Authors: Pietro Folco, Laetitia Gauvin, Michele Tizzoni, Michael Szell
• Abstract summary: Urban micromobility infrastructure is typically planned ad-hoc and at best informed by survey data. We introduce a parameter that tunes the focus between demand-based and safety-based development. We show how a data-driven process can provide urban planners with automated assistance for variable short-term scenario planning.
• Score: 0.688204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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