Abstract
We propose RoadNetGAN, a road network generation method as an extension to NetGAN, a generative model that can generate graphs similar to real-world networks with the acquisition of similarity measure through learning. Our main contribution is twofold. Firstly, we added displacement attributes to the random walks to generate not only the sequence but also the spatial position of nodes as intersections within a road network to be generated, which increases the diversity of generated road network patterns including the shape of the city blocks. Secondly, we make the generator and discriminator neural networks conditional. This allows for learning of the specification of the initial node of random walks over a graph, which is especially important for interactive road network generation that is mostly used in the applications for urban planning of road networks. We demonstrate that the proposed method can generate road networks that mimic the real road networks with the desired similarity.
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Owaki, T., Machida, T. (2020). RoadNetGAN: Generating Road Networks in Planar Graph Representation. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_61
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DOI: https://doi.org/10.1007/978-3-030-63820-7_61
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