Selection of Salient Features for Route Directions

People navigating in unfamiliar environments rely on wayfinding directions, either given by people familiar with the place, or given by maps or wayfinding services. The essential role of landmarks in human route communication is well-known. However, mapping the human ability to select landmarks ad hoc for route directions to a computational model was never tried before. Wayfinding services manage the problem by using predefined points of interest. These points are not automatically identified, and they are not related to any route. In contrast, here a computational model is presented that selects salient features along a route where needed, e.g., at decision points. We propose measures to formally specify the salience of a feature. The observed values of these measures are subject to stochastical tests in order to identify the most salient features from datasets. The proposed model is implemented and checked for computability with a use case from the city of Vienna. It is also crosschecked with a human subject survey for landmarks along a given route. The survey provides evidence that the proposed model selects features that are strongly correlated to human concepts of landmarks. Hence, integrating the selected salient features in wayfinding directions will produce directions with lower cognitive workload and higher success rates, compared to directions based only on geometry, or on geometry and static points of interest.