Grid Pattern Recognition in Road Networks Based on Graph Convolution Network Model

Road networks are the skeleton of a city, its pattern recognition plays an important role in urban landscape analysis, municipal planning, and traffic flow analysis. Road networks pattern recognition is an intelligent recognition problem that combines road geometric features, semantic features, and contextual relationships. The current road pattern recognition methods are mainly using geometric measures and statistical indicators to detect pattern recognition rules, and based on the edge-node graph structure and the polygon group structure of road network.As the expression of road patterns is influenced by human spatial cognition and visual psychology, rule-based reasoning and statistical analysis are difficult to obtain results consistent with human judgment. Driven by artificial intelligence technology, this paper introduces a novel deep learning model for graph structure, namely graph convolution network (GCN), to identify the grid-pattern in a road network. Firstly, to construct the graph structure, the road intersections are taken as nodes and the roads are taken as edges, all nodes are classified into grid and non-grid to act as the label and prediction target of the model. Road segments are then broken into consecutive linear units to extract the characteristics of the nodes which are also the input of the model. Then, the grid pattern is recognized by extracting the roads whose endpoints are all predicted to be a grid. The experimental results show that the accuracy of the method can reach 89.2% for road network's node classification and 86.1% for road segment classification, so it is effective for identifying grid pattern in a road network.