Do You Need Instructions Again? Predicting Wayfinding Instruction Demand

How to Cite?

Alinaghi, N., Kwok, T. C., Kiefer, P., & Giannopoulos, I. (2023, September). Do You Need Instructions Again? Predicting Wayfinding Instruction Demand. In GIScience 2023.

Abstract

The demand for instructions during wayfinding, defined as the frequency of requesting instructions for each decision point, can be considered as an important indicator of the internal cognitive processes during wayfinding. This demand can be a consequence of the mental state of feeling lost, being uncertain, mind wandering, having difficulty following the route, etc. Therefore, it can be of great importance for theoretical cognitive studies on human perception of the environment. From an application perspective, this demand can be used as a measure of the effectiveness of the navigation assistance system. It is therefore worthwhile to be able to predict this demand and also to know what factors trigger it. This paper takes a step in this direction by reporting a successful prediction of instruction demand (accuracy of 78.4%) in a real-world wayfinding experiment with 45 participants, and interpreting the environmental, user, instructional, and gaze-related features that caused it.

Material

All data is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, and all software files are licensed under the MIT License. 

• Data: A CSV file containing 75 computed features for classifying instruction demand. These include 41 Environmental, 16 Instruction-, 12 User-related, and 6 Gaze features. Detailed explanations of all features and how they are computed are provided in the accompanying paper. But here you can see a summary of these features: 
  • Environmental Features:
    • unified-segment
      • distance from/to previous/next turn junctions
      • distance from/to previous/next non-turn junctions
      • segment-length
      • route-length
      • time passed since start
    • landuse
    • PoI
  • User Features:
    • demographics
      • gender (binary)
      • age (in years)
      • familiarity (binary)
    • Big Five Personality traits
    • Spatial Strategies Questionnaire FRS
  • Instruction Features
    • length-related
      • number of words
      • number of characters
    • content-related
      • OSM PoI
      • landmark OSM type
      • contains-street-names (boolean)
      • last instruction (boolean)
  • Gaze Features
    • fixation count
    • min/max/sd fixation
    • mean fixation duration
    • fixation duration skewness

• Code: The analysis code used in the paper is available as a Jupyter Notebook.