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Enhancing wayfinding and route learning: a human-centered study of route-defining locations algorithm
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0002-5367-5322
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0001-5629-0981
2024 (Engelska)Ingår i: Behaviour & Information Technology, ISSN 0144-929XArtikel i tidskrift (Övrigt vetenskapligt) Epub ahead of print
Abstract [en]

Navigation services play a crucial role in everyday wayfinding. One of their key features is the turn-by-turn guidance, enabling wayfinders to navigate efficiently between locations. However, over-reliance on turn-by-turn guidance can hinder wayfinders' ability to perceive their surroundings and develop spatial awareness, negatively impacting user experience. Previous research has addressed these issues by modifying instructions, user interaction, or both. However, the applicability of these methods beyond specific studies remains unclear. This paper presents a human-centred study by evaluating an algorithm that automatically generates navigation instructions. The instructions are generated by adding route-defining locations to ‘standard’ turn-by-turn instructions to enhance wayfinder's spatial knowledge acquisition, and improve user experience. Route-defining locations, identified using a previously proposed algorithm, serve as anchors for the wayfinder to form a spatial understanding of the environment. The study involved 36 participants divided into two groups: one received ‘standard’ turn-by-turn (TBT) instructions, while the other received instructions that included route-defining locations (RDL). Participants navigated in an unfamiliar virtual environment, and their wayfinding and route learning were compared between the two groups. Results show that RDL instructions led to better wayfinding and route learning performance compared to TBT instructions, suggesting the potential to improve navigation systems and user experiences.

Ort, förlag, år, upplaga, sidor
Taylor & Francis, 2024.
Nyckelord [en]
Human-centred study, routedefining locations, wayfinding, route learning, human-computer interaction
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:umu:diva-206803DOI: 10.1080/0144929X.2024.2308754ISI: 001166916400001Scopus ID: 2-s2.0-85185492298OAI: oai:DiVA.org:umu-206803DiVA, id: diva2:1751201
Ingår i projekt
Hur man undgår ?Death by GPS?: Grunderna för adaptiv navigeringshjälp, Vetenskapsrådet
Forskningsfinansiär
Vetenskapsrådet, 2018-05318
Anmärkning

Originally included in thesis in manuscript form.

Tillgänglig från: 2023-04-17 Skapad: 2023-04-17 Senast uppdaterad: 2024-07-22
Ingår i avhandling
1. Escaping 'death by GPS': foundations for adaptive navigation assistance
Öppna denna publikation i ny flik eller fönster >>Escaping 'death by GPS': foundations for adaptive navigation assistance
2023 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[sv]
Att undkomma "döden med GPS" : grunderna för adaptiv navigeringshjälp
Abstract [en]

Navigating through physical environments has evolved over time from using stars and maps to support the wayfinding, to employing Global Positioning Systems and navigation services. Turn-by-turn guidance of navigation services is an effective way to support wayfinding, but it may not align with the way humans naturally navigate. Over-reliance on navigation services can lead to confusion, frustration, and even dangerous situations. Humans use environmental cues to support their navigation decisions and understand their position, orientation, and surroundings. Navigation services prioritize efficient route planning and may not consider factors, such as complexity, that can impact travel. This discrepancy between navigation services and human navigation highlights the importance of incorporating principles of human wayfinding into navigation systems to enhance the overall wayfinding experience.

This thesis aims to improve navigation services by exploring their adaptive capabilities and addressing the discrepancies between navigation services and human wayfinding. The research focuses on identifying difficult-to-navigate intersections and prominent locations along a route that are important for successful navigation, and developing automated ways to identify them. The thesis also explores adapting instruction giving to the route and its surrounding.

The research included in this thesis analyzed geographic data, developed models and measures that extended existing research, and conducted empirical human subject studies. This work developed models that optimize route search for specific criteria, including traffic and social costs. It also proposes approaches to identifying and simplifying prominent locations along a route that define the relationship between the route and the environment. Results show that people tend to prefer less complex routes with fewer prominent locations. Results also indicate that incorporating route-defining locations in route directions can aid wayfinders in forming useful spatial memory of the environment. Additionally, the studies identified the language used and spatial reasoning mechanisms as sources of mismatches between navigation instructions and human understanding of a given wayfinding situation, which may provide insights into improving the generation of instructions.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå University, 2023. s. 54
Serie
UMINF, ISSN 0348-0542 ; 23.03
Nyckelord
wayfinding, navigation systems, navigation complexity, prominent locations, route generalization, spatial cognition, mental models, route learning, direction giving, Human-centered study.
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:umu:diva-206805 (URN)978-91-8070-025-2 (ISBN)978-91-8070-024-5 (ISBN)
Disputation
2023-05-11, MIT.A.121, Umeå, 09:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2023-04-20 Skapad: 2023-04-17 Senast uppdaterad: 2023-04-17Bibliografiskt granskad

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Teimouri, FatemeRichter, Kai-Florian

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