Finally, we propose a metric to quantify the diversity of the predictions. We argue that an algorithm producing more routes is not necessarily better than an algorithm producing fewer routes, because the routes can be highly similar. To compute the diversity, we compute the pairwise distance matrix of all the predicted routes using the fast machine learning method previously described , and then we use hierarchical clustering to group the routes. We optimize the number of clusters using the Silhouette method and the optimal number of clusters is viewed as a metric of diversity.