Detecting interchanges in road networks using a graph convolutional network approach

Abstract

Detecting interchanges in road networks benefit many applications, such as vehicle navigation and map generalization. Traditional approaches use manually defined rules based on geometric, topological, or both properties, and thus can present challenges for structurally complex interchange. To overcome this drawback, we propose a graph-based deep learning approach for interchange detection. First, we model the road network as a graph in which the nodes represent road segments, and the edges represent their connections. The proposed approach computes the shape measures and contextual properties of individual road segments for features characterizing the associated nodes in the graph. Next, a semi-supervised approach uses these features and limited labeled interchanges to train a graph convolutional network that classifies these road segments into an interchange and non-interchange segments. Finally, an adaptive clustering approach groups the detected interchange segments into interchanges. Our experiment with the road networks of Beijing and Wuhan achieved a classification accuracy >95% at a label rate of 10%. Moreover, the interchange detection precision and recall were 79.6 and 75.7% on the Beijing dataset and 80.6 and 74.8% on the Wuhan dataset, respectively, which were 18.3–36.1 and 17.4–19.4% higher than those of the existing approaches based on characteristic node clustering.

Keywords:

• Interchange detection
• graph convolutional network
• road network
• graph convolution
• road shape context

Acknowledgments

The authors sincerely appreciate all insightful and constructive comments and suggestions from the editors and anonymous reviewers, which significantly improved the quality of this paper.

Data and codes availability statement

The data and codes that support the findings of this study are available in Figshare at https://doi.org/10.6084/m9.figshare.14605425.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 41871377, 42001415, 42071450].

Notes on contributors

Min Yang

Min Yang is an Associate Professor at the School of Resource and Environmental Sciences, Wuhan University, Wuhan, China. He received the B.S. and Ph.D. degrees in cartography from Wuhan University in 2007 and 2013, respectively. His research interests include map generalization, machine learning, and spatial big data analysis.

Chenjun Jiang

Chenjun Jiang is a post-graduate student majoring in geographic information systems from the School of Resource and Environmental Sciences, Wuhan University, Wuhan, China. His research interests include map generalization and intelligent processing of map data.

Xiongfeng Yan

Xiongfeng Yan is currently a post-doctoral researcher at the College of Surveying and Geo-Informatics, Tongji University, Shanghai, China. He received the B.S. and Ph.D. degrees in cartography from Wuhan University in 2012 and 2019, respectively. His research interests include cartography and machine learning.

Tinghua Ai

Tinghua Ai is a Professor at the School of Resource and Environmental Sciences, Wuhan University, Wuhan, China. He received a Ph.D. degree in cartography from the Wuhan Technical University of Surveying and Mapping in 2000. His research interests include multi-scale representation of spatial data, spatial cognition, and spatial big data analysis.

Minjun Cao

Minjun Cao is a post-graduate student majoring in cartography from the School of Resource and Environmental Sciences, Wuhan University, Wuhan, China. His research interests include map generalization and machine learning.

Wenyuan Chen

Wenyuan Chen is a post-graduate student majoring in cartography from the School of Resource and Environmental Sciences, Wuhan University, Wuhan, China. Her research interests include cartography and land-use database.