Structural invariants in street networks

Streets networks are the main mobility channels in cities, allowing residents to navigate the different urban components. Since navigability is a key ingredient of socioeconomic activity, roads represent one of its most important infrastructural components and a large body of work has elucidated its structure. One such metric, intricately related to the flow of people and goods and services, is the betweenness centrality. The betweenness, a path-based global measure of flow, is a static predictor of congestion and load on networks. We demonstrate that its statistical distribution is invariant for any street network in any city irrespective of topography, geography or urban planning choices. This invariance is a consequence of spatial embedding of the street network in a 2D plane leading to an underlying tree structure for high betweenness nodes that controls the majority of the flow. Furthermore, these high congestion streets display increasing spatial correlation as a function of increasing density of streets. Counterintuitively building more streets does not alleviate congestion but diverts it further to the city center. Urban policy planners are thus better served in investing in multimodal transportation systems and building overpasses, underpasses and multilayered roads than merely building more traditional connectivity. We confirm our analysis through empirical results on street networks from 97 cities worldwide as well as 200 years of street data for Paris.