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

Tian Lan,Chengyin Gong,Zhilin Li,Peng Ti,Jicheng Wang

doi:10.3390/ijgi11010036

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.

Highlights

Published: 5 January 2022Schematic network maps are popular for the representation of transport networks.Such maps provide linear abstractions of functional networks [1], in which the relationships between the edges are of more consequence than the geographical position, size, or shape [2]
It is found that the name labels placed by Maplex have the largest number of overlaps in all three metro networks (i.e., 67, 20, and 10 in Beijing, Tianjin, and Hong Kong metro, respectively), while those placed by the artificial neural networks (ANNs)-based method have the smallest number of overlaps (i.e., 8, 0, and 0 in Beijing, Tianjin, and Hong Kong metro, respectively)
The progressive strategy for avoiding overlap in the ANNs-based method considers all types of overlap

Summary

Introduction

Published: 5 January 2022Schematic network maps (often called schematic maps) are popular for the representation of transport networks (e.g., metro, bus, and high-speed railway networks).Such maps provide linear abstractions of functional networks [1], in which the relationships between the edges (such as topological relationships) are of more consequence than the geographical position, size, or shape [2]. One famous example of schematic maps is the London Underground map designed by Harry Charles Beck in the 1930s, where congested areas are enlarged, and lines are re-orientated along octilinear (i.e., horizontal, vertical, and diagonal) directions with the preservation of topological relationships. Such a design improves the clarity and readability of the map, allowing people to quickly and accurately perform route planning and orientation tasks. The automated generation of schematic maps is treated as an optimization problem, and most researchers prefer to optimize network layouts

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