Walk and learn: effects of human-centered navigation systems on pedestrians’ navigation behavior

A city of the future is likely to be one where people, sensors, and the environment fuse together into one interconnected, dependent, and intelligent system - a smart city. Accordingly, navigation in a smart city will also change drastically, as intelligent navigation systems will autonomously guide people from one place to another. Using these automated systems, people will be able to navigate to their destination more efficiently, but these systems also appear to decrease cognitive development relevant to learning and memory in everyday activities. By relying more on a navigation system rather than relevant features in the environment, humans may lose the ability to acquire spatial knowledge that, amongst other things, ensures brain development. It is known that people develop these essential cognitive abilities for everyday situations when using analog maps. However, little is known about how analog and automated navigation assistance can be combined to ensure efficient navigation without losing the ability to acquire spatial knowledge. This thesis aims to understand the relationship between a human navigator and partly automated navigation systems in real-world environments, as most navigation experiments up until now have only assessed human behavior and spatial knowledge acquisition in virtual labs. In order to ensure that experiments are based around real-life situations, I have developed an experimental ’WALK-AND-LEARN’ framework, which evaluates human navigation behavior and spatial knowledge acquisition in urban, real-world environments. The framework consists of an assisted route-following (incidental spatial knowledge acquisition) and an unassisted route-reversal (spatial knowledge recall) task. The framework allows pedestrians to use all their senses in-situ during both the learning and testing phases. This should account for the shortcomings of traditional setups, such as controlled lab experiments. Two experiments assessed pedestrian interactions with various navigation systems and with the environment traversed. I designed partly automated navigation systems by systematically analyzing cognitive processes between human-centered (a low level of automation) and system-centered (a high level of automation) navigation system designs. Data on navigation performance, interactions with the navigation system, and mobile eye-tracking recordings build the basis for the spatial analysis of pedestrian behavior in the real world. The methodology provides unique insights into the behavior of pedestrians using navigation systems in urban environments. The results show how human-centered navigation assistance can be designed to ensure a high level of navigation efficiency without losing the ability to acquire spatial knowledge. Specifically, the results show that a system that demands proactive human decision-making can assist navigators in incidentally acquiring spatial knowledge, while a system using context-dependent information does not. This thesis shows that participants who are tested in-situ can use all their senses to acquire and to recall spatial knowledge. In addition, and more importantly, this thesis also reveals the significance of the complexity of the traversed environment, such as traffic, in understanding pedestrian behavior and the acquisition of spatial knowledge in real-world navigation tasks. The analyses in this thesis have uncovered distinctive environment-dependent patterns of map interaction behavior, for example map rotations at turns, and gaze behavior, for example varying attention to the navigation system and to the environment, along the routes traversed. In conclusion, the empirical findings emphasize the importance of understanding human navigation behavior in relation to automatic (intelligent) navigation systems in real-world environments in order to ensure human skill development that will be necessary for everyday activities in a prospectively smart, autonomous city