Talk at NetSci 2019 at the University of Vermont, Burlington, VT. USA.
Urban transportation networks, from sidewalks and bicycle paths to streets and rail lines, provide the backbone for movement and socioeconomic life in cities. These networks can be understood as layers of a larger multiplex network. Because cities are car-centric, the most developed layer is typically the street layer, while other layers can be highly disconnected. To make urban transport sustainable, an increasing number of cities are prioritizing the development of their bicycle networks. However, given the usually patchy nature of the bicycle network layer, it is yet unclear how to extend it comprehensively and effectively given a limited budget. Here we develop algorithmic network growth strategies and apply them to multiple cities around the world, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks. We motivate the development of our algorithms with a network component analysis and with multimodal urban fingerprints that reveal different classes of cities depending on the connectedness between different network layers. We introduce two greedy algorithms to add the most critical missing links in the bicycle layer: the first connects the two largest connected components, while the second connects the largest with the closest component. In terms of connectedness and directness, we show that our algorithms considerably outperform both a random approach and a baseline minimum investment strategy that connects the closest components ignoring size. This computational method outlines novel pathways from car-centric towards sustainable cities by taking advantage of urban data available on a city-wide scale. Our algorithms are a first step towards a quantitative consolidation of bicycle infrastructure development that can become valuable for urban planners and stakeholders.