Route network simplification for transport planning

Route network datasets are fundamental to transport models, serving as both inputs for analysis and outputs for visualization and decision-making. The increasing complexity of route network data from sources like OpenStreetMap allows for more detailed modelling of sustainable transport modes such as walking and cycling. However, this level of detail can introduce challenges for the clear visualization and interpretation of model results. A common problem is the representation of single transport corridors by multiple parallel lines, which can create visual clutter and obscure important patterns in transport flows. The purpose of the work presented in this paper is to provide a basis for computationally efficient analysis and visualization of route networks for strategic transport planning, where intricate geometries, such as parallel or ‘braided’ linestrings, are unhelpful. We present and evaluate two distinct methods for simplifying complex route networks that are intended to be used as a ‘pre-processing’ step to speed up and improve the results of strategic transport network analysis, modelling, and visualization workflows. First, we present skeletonization, an approach that uses ‘thinning’ of rasterized network data to extract a simplified representation of the network. Second, we present a Voronoi-based approach using Voronoi diagrams to identify centrelines. We demonstrate the practical application of these methods using the ‘Simplified network’ layer in the Transport for Scotland-funded Network Planning Tool, a publicly accessible resource at https://www.npt.scot. To support reproducible research, we implement the methods in the open-source parenx Python package, enabling their use alongside other open source tools for transport planning, research, and educational applications.