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.

Data-driven micromobility network planning for demand and safety / Folco, Pietro; Gauvin, Laetitia; Tizzoni, Michele; Szell, Michael. - In: ENVIRONMENT AND PLANNING. B, URBAN ANALYTICS AND CITY SCIENCE. - ISSN 2399-8083. - 2022:(2022). [10.1177/23998083221135611]

Data-driven micromobility network planning for demand and safety

Tizzoni, Michele
Penultimo
;
2022-01-01

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.
2022
Folco, Pietro; Gauvin, Laetitia; Tizzoni, Michele; Szell, Michael
Data-driven micromobility network planning for demand and safety / Folco, Pietro; Gauvin, Laetitia; Tizzoni, Michele; Szell, Michael. - In: ENVIRONMENT AND PLANNING. B, URBAN ANALYTICS AND CITY SCIENCE. - ISSN 2399-8083. - 2022:(2022). [10.1177/23998083221135611]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/362244
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