City Futures - Research Lab

The research projects explore alternative narratives of mobility and establish new knowledge.

The team at MIC-HUB engages with interdisciplinary partners and universities to translate questions into insights.

MIC-HUB also offers a growing range of in-house services through digital developments, delivering advanced data visualisations, scientific analysis, and digital community engagement solutions. We offer valuable insights into evidence-based decision-making processes, enabling key decision-makers to gain a greater understanding of mobility scenarios and forecasts.

We adopt a hands-on approach to develop effective digital solutions that are tailored to your needs and resources, combining technological expertise with subject matter knowledge.

Rapid technological advances coupled with shifts in demographics and public preferences are dramatically altering the nature of transportation in cities.

MIC-HUB assists clients by harnessing emerging technologies and models in areas like the formulation of automotive, micro-mobility and smart mobility strategies.

MIC-HUB develops machine learning (ML) algorithms and models to analyse urban mobility phenomena and their interaction with the built environment, providing predictive tools to support decision-making and sustainable mobility planning. MIC-HUB’s models can predict traffic flows, travel behaviours, and road safety risk levels by processing big data from sensors, GPS, social media, and traffic simulations. This contributes to greater efficiency, safety and sustainability in transport systems.

ML algorithms help decode the relationships between mobility and the built environment in terms of urban density, land use, and street design, revealing how these factors influence travel choices, distances, transport modes, and externalities such as crash rates and emission levels.

MIC-HUB uses supervised learning algorithms, including neural networks, to develop predictive models that assess traffic volumes, network speeds, and accident rates, based on the urban and sociodemographic characteristics of each study area.

Unsupervised machine learning techniques use cluster analysis and dimensionality reduction to identify unknown patterns. These analyses help to reveal hidden structures within complex, heterogeneous data on mobility and other urban characteristics, such as chains of human movement.