Identification of communities in urban mobility networks using multi-layer graphs of network traffic

This paper proposes a novel approach to identify the pockets of activity or the community structure in a city network using multilayer graphs that represent the movement of disparate entities (i.e. private vehicles, buses and passengers) in the network. First, we process the trip data corresponding to each entity through a Voronoi segmentation procedure which provides a natural null model to compare multiple layers in a real world network. Second, given nodes that represent Voronoi cells and link weights that define the strength of connection between them, we apply a community detection algorithm and partition the network into smaller areas independently at each layer. The partitioning algorithm returns geographically well connected regions in all layers and reveal significant characteristics underlying the spatial structure of our city