Understanding Information Requirements in “Text Only” Pedestrian Wayfinding Systems

  • William Mackaness
  • Phil Bartie
  • Candela Sanchez-Rodilla Espeso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8728)


Information that enables an urban pedestrian to get from A to B can come in many forms though maps are generally preferred. However, given the cognitive load associated with map reading, and the desire to make discrete use of mobile technologies, there is increasing interest in systems that deliver wayfinding information solely by means of georeferenced spoken utterances that essentially leave the user “technology free.” As a critical prior step, this paper examines the optimal delivery of such georeferenced text based instructions in anticipation of their spoken utterance. We identify the factors governing the content, location of instruction and frequency of delivery of text instructions such that a pedestrian can confidently follow a prescribed route, without reference to a map. We report on street level experiments in which pedestrians followed a sequence of text instructions delivered at key points along a set of routes. In examining instructions that are easy to follow, we compare landmark based instructions with street name based instructions. Results show that a landmark based approach is preferred because it is easier to assimilate (not because it is faster). Analysis also revealed that some degree of redundancy in the instructions is required in order to bring “comfort” to the user’s progress. There still remains the challenge of modeling the saliency of landmarks, knowing what is the most efficient set of instructions, and how to vary the frequency of instruction according to the complexity of the route. The paper concludes by identifying a set of design heuristics useful in the design of text based instructions for wayfinding.


Cognitive Load Situational Awareness Salient Object City Model Urban Morphology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • William Mackaness
    • 1
  • Phil Bartie
    • 1
  • Candela Sanchez-Rodilla Espeso
    • 1
  1. 1.School of GeoSciencesThe University of EdinburghEdinburghUK

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