Streets networks are the primary facilitators of movement in urban systems, allowing residents to navigate the different functional components of a city. 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 structural properties. Yet more than the physical layout, it is the sampling of street networks that serves as a true fingerprint of the complex interactions between people, and the flow of goods and services in urban systems, a feature of which there is limited understanding.

To fill this gap, we conducted a systematic mesoscale study of street morphology (shape of sampled routes) through the introduction of a novel metric that we term Inness. The Inness encapsulates the direction, orientation, and length of routes, thus revealing the morphology of connectivity in street networks, including the distribution of implicit socioeconomic forces that may inform routing choices. In particular, this metric enables us to put functions of individual streets in the context of the dynamics of the whole city (Broadway or Fifth Avenue in NYC, for instance), linking local structures to the large-scale urban organization.  

The dynamics of a city, of course, is intricately related to the flow of people and goods and services, a structural measure of which is the betweenness centrality. The betweenness centrality, 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 the 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.