Structure and Coherence in City Road Network Generation

We analyze two popular tile-based Procedural Content Generation (PCG) methods, WaveFunctionCollapse and transformers, to explore different notions of conditional probability and their significant effect on the quality of results. Specifically, we seek to answer the question of which method produces higher-quality results for generating city road networks as 2D images. Road networks are an interesting domain since they have large scale structures which require consistency across many tiles. To experiment with this, first we use OpenStreetMap to create a dataset of 2D tiled images with additional information on the type of each road encoded in the tile tokens. We use this dataset to train two WFC models with different decision heuristics, and an additional transformer model with a sliding window inference process. We then compare the results of these models using a set of tile-based metrics (e.g. tile-frequency resemblance and edge-frequency resemblance) and urban-planning metrics (e.g. node density, road connectivity, etc.). Our results show that the transformer model outperforms the WFC methods in terms of generating high-quality city road networks, and demonstrate the potential of transformers for tile-based PCG methods, especially when a considerable amount of data is available.