  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
transport network. Because most 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, cities are increasingly
investing to develop 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
data-driven, algorithmic network growth strategies and apply them to 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 algorithm connects the two largest
connected components, the second algorithm connects the largest with the
closest component. We show that these algorithms outmatch both a random
approach and a baseline minimum investment strategy that connects the closest
components ignoring size. Our computational approach outlines novel pathways
from car-centric towards sustainable cities by taking advantage of urban data
available on a city-wide scale. It is a first step towards a quantitative
consolidation of bicycle infrastructure development that can become valuable
for urban planners and stakeholders.
