Route planning algorithms attempt to find the optimum path between two nodes in a graph, where a cost function specifies a weight for each edge. In many situations cost is related to distance or time. While time estimates are rather straightforward for automotive applications, real-world route planning for hiking and other outdoor activities requires careful consideration of a variety of different factors in order to produce reliable time estimates. Static estimates such as Naismith's Rule for estimating hike times do not consider individual factors such as a hiker's fitness or current progress along the trail. In this paper we address these aspects and develop a model for individual weight estimation that can be exploited in route planning applications. An evaluation conducted on GPS traces from hikes in South Tyrol, Italy indicates that the model can outperform Naismith's estimate by up to 23%.