|Jutta Willamowski, Stéphane Clinchant, Christophe Legras, Sofia Michel, Shreepriya Shreepriya|
|HCII 2019, Orlando, USA, 26-31 July, 2019|
In this paper, we present an approach to generate pleasant running tours for unknown environments. We start from a first version, able to generate basic pleasant tours, but without explicitly catering for elevation. Based on observations on how runners appreciate elevation gain and steepness and on interviews with local runners, we identified corresponding additional needs. Indeed, runners usually have specific implicit or explicit objectives or limits with respect to the elevation gain they want to target or to avoid with a tour. In consequence, we extend our first approach. We expose the algorithm we have defined to address elevation constraints during tour generation and the underlying intuitions. An important differentiator with prior art is that we address elevation constraints during the tour generation phase and not a posteriori. This means that our approach is able to efficiently generate new tours that match the user’s constraints, while prior art rather finds matching tours by searching a posteriori among a set of available tours, and only finds matching ones if they are already present.
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