Route schematization with landmarks

Abstract

Modern navigation applications make use of a turn-by-turn instructions approach and are mostly supported by digital devices with limited display size. This combination does little to improve users' orientation or spatial knowledge acquisition. Considering this limitation, we propose a route schematization method to facilitate the readability of route information and survey knowledge acquisition. Current schematization methods focus on the route path and ignore context information, specially polygonal landmarks such as lakes, parks, and regions, which is crucial for promoting orientation. Our schematization method, in addition to the route path, takes as input: adjacent streets, point-like landmarks, and polygonal landmarks. Moreover, the schematic layout highlights spatial relations between route and context information, improves the readability of turns at decision points, and the visibility of the surroundings. The focus of the paper is the schematization method that combines geometric transformations and integer linear programming to produce the maps. Two routes are used as examples to present the execution information and the outputs. We complement our results with a user study that indicates a preference for our schematic layout in matching textual route instructions. The contribution of this paper is a method that produces schematic route maps with context information to support the user in wayfinding and orientation.

Evaluating schematic route maps in wayfinding tasks for in-car navigation

“…Galvão et al ( 2020 ) published a route schematization method that fills this gap by considering global and polygonal landmarks. Computationally, the method is based on the wayfinding chorems theory that had been formulated by (Klippel et al, 2005 ).…”

“…The theory was further extended by Richter ( 2008 ) who produced optimized, cognitively adequate wayfinding choremes that consider landmarks and their spatial relation with the route. The map schematization method proposed by Galvão et al ( 2020 ) implements these features using integer linear programming to optimize the schematic layout design while preserving the correct topology. The method considers both point-like and polygonal landmarks together with their spatial relations with the route.…”

“…However, they presented no experimental quantitative data to support this. In the present article, we empirically test route maps produced by Galvão et al ( 2020 ) algorithm in two distinct navigation tasks. We focus on the human-computer interaction with the map application and the map’s impact on spatial learning.…”

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Figure 1. Example of the input (non-schematized) and output (schematized) route map produced by the method described by Galvão et al ( 2020 )

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“…In previous work, we presented a route schematization method that follows design rules of cognitively motivated wayfinding instructions (Galvão et al, 2020 ). This schematization method has an advantage over other methods because it considers polygonal landmarks and global landmarks.…”