Urban cycling benefits personal wellbeing, public health, and global sustainability. While current tools such as Google and Apple Maps provide bike route recommendations, they do not account for a person’s dynamic context (e.g., commuting, recreation). We introduce BikeButler, a personalized, context-sensitive bicycle route generation tool that enables users to generate, compare, virtually preview, and iteratively customize bike routes via custom profiles that encode seven bikeability features, including bike lane existence, slope, vegetation, and surface quality—fusing data from OpenStreetMap, open government data, and a custom VLM-based analysis of Street View images. To design BikeButler, we employed a human-centered, iterative approach starting with formative interviews and culminating in a user study (N=16). Our findings demonstrate that bike routing preferences change as a function of context, that BikeButler enables users to quickly create and iterate context-sensitive routes, and that generated routes differ significantly from Google Maps bike routing, reinforcing the importance of personalization.
ACM CHI Conference on Human Factors in Computing Systems