Recently, the adoption of micromobility as an alternative mode of transportation on a large scale has been
growing rapidly. However, its operational and safety aspects have not been extensively investigated in the
literature. Following this purpose, we developed a novel methodology that aims at evaluating priority areas for
shared micromobility system users’ accident risk mitigation based on predicted injury severity using a machine
learning-based approach. The methodology proposed in this paper consists of two models: a predictive model,
which is based on an artificial neural network with a pattern recognition algorithm, to estimate the expected
safety indicator of an urban zone, and a clustering method to define the priority areas for intervention through
the application of a geofence speed regulation system. A real case study was carried out in the city of Bari, Italy,
to test the effectiveness of the proposed methodology. The results showed how it is possible to define areas for
intervention with different priorities based on the expected severity index. The proposed methodology can be
seen as a decision support system to assist transport operators and urban planners in regulating shared micromobility
vehicles in urban areas by defining priority areas for intervention through geofencing and, therefore, it
can be useful for improving micromobility adoption, road safety, and urban mobility policies
