Abstract
The pathways used by cyclists, pedestrians, and users of micromobility to cross intersections do not always align with those planned by traffic engineers. Observing actual usage patterns could lead to a better understanding of the tactical behavior of users of active and micromobility, allowing planners and engineers to create urban environments specifically for these road users. An open-source Python tool is introduced that uses clustering to automatically identify the forms of pathways used by road users. The tool was used to cluster trajectories from five intersections in Germany. The exemplar of each cluster is selected to represent the average shape of each pathway type. The open-source Python tool RoadUserPathways is introduced, the case studies are examined and use cases are presented.
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