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Do You Need Instructions Again? Predicting Wayfinding Instruction Demand

Authors Negar Alinaghi , Tiffany C. K. Kwok , Peter Kiefer , Ioannis Giannopoulos

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Author Details

Negar Alinaghi
  • Geoinformation, TU Wien, Austria
Tiffany C. K. Kwok
  • Institute of Cartography and Geoinformation, ETH Zürich, Switzerland
Peter Kiefer
  • Institute of Cartography and Geoinformation, ETH Zürich, Switzerland
Ioannis Giannopoulos
  • Geoinformation, TU Wien, Austria, Institute of Advanced Research in Artificial Intelligence (IARAI), Austria

Acknowledgements

We would like to thank our colleagues from Vienna University of Technology, Dr. Markus Kattenbeck, for suggesting the use of the Big Five Personality Traits test, and Antonia Golab for collecting the valuable data used for this work.

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Negar Alinaghi, Tiffany C. K. Kwok, Peter Kiefer, and Ioannis Giannopoulos. Do You Need Instructions Again? Predicting Wayfinding Instruction Demand. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 1:1-1:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.1

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Activity recognition and understanding
  • Computing methodologies → Supervised learning by classification
Keywords
  • Wayfinding
  • Navigation Instructions
  • Urban Computing
  • Gaze Analysis

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