An ANNs-Based Method for Automated Labelling of Schematic Metro Maps

Abstract

Schematic maps are popular for representing transport networks. In the last two decades, some researchers have been working toward automated generation of network layouts (i.e., the network geometry of schematic maps), while automated labelling of schematic maps is not well considered. The descriptive-statistics-based labelling method, which models the labelling space by defining various station-based line relations in advance, has been specially developed for schematic maps. However, if a certain station-based line relation is not predefined in the database, this method may not be able to infer suitable labelling positions under this relation. It is noted that artificial neural networks (ANNs) have the ability to infer unseen relations. In this study, we aim to develop an ANNs-based method for the labelling of schematic metro maps. Samples are first extracted from representative schematic metro maps, and then they are employed to train and test ANNs models. Five types of attributes (e.g., station-based line relations) are used as inputs, and two types of attributes (i.e., directions and positions of labels) are used as outputs. Experiments show that this ANNs-based method can generate effective and satisfactory labelling results in the testing cases. Such a method has potential to be extended for the labelling of other transport networks.