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What determines a boundary for navigating a complex street network: evidence from London taxi drivers

Published online by Cambridge University Press:  06 October 2021

Eva-Maria Griesbauer Open the ORCID record for Eva-Maria Griesbauer [Opens in a new window] ,
Ed Manley ,
Daniel McNamee ,
Jeremy Morley  and
Hugo Spiers
Eva-Maria Griesbauer*
Affiliation:
University College London, London, UK.
Ed Manley
Affiliation:
University of Leeds, Leeds, UK.
Daniel McNamee
Affiliation:
University College London, London, UK.
Jeremy Morley
Affiliation:
Ordnance Survey, Southampton, UK
Hugo Spiers
Affiliation:
University College London, London, UK.
*
*Corresponding author. E-mail: [email protected]
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Abstract

Spatial boundaries play an important role in defining spaces, structuring memory and supporting planning during navigation. Recent models of hierarchical route planning use boundaries to plan efficiently first across regions and then within regions. However, it remains unclear which structures (e.g. parks, rivers, major streets, etc.) will form salient boundaries in real-world cities. This study tested licensed London taxi drivers, who are unique in their ability to navigate London flexibly without physical navigation aids. They were asked to indicate streets they considered as boundaries for London districts or dividing areas. It was found that agreement on boundary streets varied considerably, from some boundaries providing almost no consensus to some boundaries consistently noted as boundaries. Examining the properties of the streets revealed that a key factor in the consistent boundaries was the near rectilinear nature of the designated region (e.g. Mayfair and Soho) and the distinctiveness of parks (e.g. Regent's Park). Surprisingly, the River Thames was not consistently considered as a boundary. These findings provide insight into types of environmental features that lead to the perception of explicit boundaries in large-scale urban space. Because route planning models assume that boundaries are used to segregate the space for efficient planning, these results help make predictions of the likely planning demands of different routes in such complex large-scale street networks. Such predictions could be used to highlight information used for navigation guidance applications to enable more efficient hierarchical planning and learning of large-scale environments.

Keywords

navigationroute planningregionspace
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 1 , January 2022 , pp. 15 - 34
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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