Automated creation of pedestrian route descriptions

Abstract

Providing unambiguous, succinct descriptions of routes for pedestrians to follow is very challenging. Route descriptions vary according to many things, such as route length and complexity, availability of easily identifiable landmarks, and personal preferences. It is well known that the inclusion of a variety of landmarks facilitates route following – either at key decision points, or as a confirmatory cue. Many of the existing solutions, however, behave like car navigation systems and do not include references to such landmarks. The broader ambition of this research is the automatic generation of route descriptions that cater specifically to the needs of the pedestrian. More specifically this research describes empirical evidence gathered to identify the information requirements for an automated pedestrian navigation system. The results of three experiments helped to identify the criteria that govern the relative saliency of features of interest within an urban environment. There are a large variety of features of interest (together with their descriptions) that can be used as directional aids within route descriptions (for example buildings, statues, monuments, hills, and roads). A set of variables were developed in order to measure the saliency of the different classes of features. The experiments revealed that the most important measures of saliency included name, size, age, and colour. This empirical work formed the basis of the development of a pedestrian navigation system that incorporated the automatic identification of features of interest using the City of Edinburgh as the study area. Additionally the system supported the calculation of the saliency of a feature of interest, the development of an intervisibility model for the route to be navigated to determine the best feature of interest to use at each decision point along the route. Finally, the pedestrian navigation system was evaluated against route descriptions gathered from a random set of individuals to see how efficiently the system reflected the more natural and richer route description that people typically generate. This work shows that modelling features of interest is the key to the automatic generation of route descriptions that can be readily understood and followed by pedestrians.