Extracting Backbone Structure of a Road Network from Raw Data

The representation of roads as a networked system of nodes and edges has attracted significant interest in the network literature, generating a large number of studies over the years. Such representation requires a proper identification of what constitute an edge or a node. Intuitively, nodes represent the junctions where roads intersect, and edges are the road segments connecting these junctions. In practice, however, such simplified presentation is not trivial to achieve due to extra details of individual roads. In this paper, we present a set of novel and efficient computational techniques based on elementary geometry and graph theory that can be employed to obtain the essential structure of a road network, while also retaining the crucial geometry of roads, such as shape and length. This is done by dissecting the network into clusters of nodes of degree other than 2 and curves, which contain consecutive nodes of degree 2, connecting these clusters. These clusters of nodes and curves will be collapsed into cleaned nodes and paths, respectively in a simplified mathematical graph. We apply this method to obtain the simplification of road network in Punggol new town in the northeast region of Singapore, and show that application of network analyses such as centrality measures could be performed in a more meaningful and concise manner than on the original road network.