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Crowdsourcing a cyclist perspective on suggested recreational paths in real-world networks

ABSTRACT Routing and navigation services for leisure activities are conditioned by special needs and trade-offs. The advent of online communities and large crowdsourced datasets offers opportunities to improve the adoption of a user’s perspective in these suggested paths. This paper focuses on achieving two goals. First, the presented methodology analyses a dataset of 190,610 historical GPS traces to gain insights into the appreciation or attractiveness of each edge in a real-world network for a specific leisure activity (i.e. road cycling). Second, as literature on these leisure activities is still sparse, we want to create a thorough understanding of the activities at hand for future work. An appreciation model is proposed and the spread of this score is analyzed in shortest-path alternatives of popular routing engines for this activity. This analysis successfully discriminates these shortest paths based on the scoring value and three morphological parameters of the path. However, the robustness of the model should be improved to ensure the viability of the proposed approach in future work. More specifically, further research on the local optimality of the route choices will be imperative.