User Behaviour in the Wild: Analysing Naturalistic Log Data of a Pedestrian Navigation App

Abstract

Abstract. Knowledge about real-life user behaviour is an important factor for the design of navigation systems. Prompted by the observation that users tend to use our campus navigation app in unexpected ways, we present a naturalistic study of navigation logs. The data set consists of sensor and interaction data from over 4600 sessions, collected over a span of several months from hundreds of users. In our analysis, we demonstrate how the core concepts from navigation literature, i. e. wayfinding and locomotion, can be observed, but also point out differences to previous studies and assumptions. One of our main findings is that the application is mostly used to plan routes in advance, and not to navigate along them. Furthermore, detailed case-studies of actual navigation sessions provide a unique insight into user behaviour and show that persons are often not focused on their navigation task but engaged otherwise. Based on these results, we formulate design implications that do not only apply to future iterations of our application, but can be seen as best practices for pedestrian navigation apps in general.

Smartphone-Based Activity Recognition in a Pedestrian Navigation Context

“…Based on earlier studies, we know that users tend to carry their device in their hands for the majority of time when navigating [ 30 ]. By applying the device placement classifier to naturalistic navigation data, we can further investigate how exactly this changes over time during a typical navigation session.…”

“…However, for this study, we are only interested in a subset of them that can be considered actual navigation sessions on the university campus. Previous studies have shown that the app is often used for looking up routes ahead of time and off campus, e.g., from their home or while riding the bus [ 30 ].…”