NEtwoRks, Data, and Society (NERDS)

Analysing cyclist behavior at signalized intersections using computer vision Artificial intelligence, in particular deep learning and computer vision, is a promising new tool for automated video analysis in traffic studies. It allows for facilitated extraction of detailed data from video recordings at higher efficiency and lower costs, improved precision of obtained data, and reduced errors […]

Network algorithms for the identification and classification of gaps in urban bicycle networks based on OSM data What is the best location to build new bicycle infrastructure in a city? This planning decision can be conceptualized as an optimization problem: the goal is to find the most efficient solution – that is, the one that […]

The geometric limits of growing urban bicycle networks City planners worldwide are increasingly realizing that cycling can be a promising solution to their unsustainable car-centric transport systems. However, common bicycle network planning practices follow piecewise ad hoc approaches which do not take into account the transportation network and its structural complexity as a whole. Here […]

Making cities better with human-centric urban data science Making our cities better and sustainable is key to solving the climate and urban transport crises. To this end, urban data science offers new tools to quantify societal problems in cities and to propose human-centric solutions to policy makers. In this talk I outline our recent and […]

Growing cohesive urban bicycle networks City planners worldwide are increasingly realizing that cycling can be a promising solution to their unsustainable car-centric transport systems. However, common bicycle network planning practices follow piecewise ad hoc approaches which do not take into account the transportation network and its structural complexity as a whole. Here we take a […]