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Uncovering Effects of Spatial and Transportation Elements on Travellers Using Biometric Data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.ORCID iD: 0000-0003-1558-382X
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.ORCID iD: 0000-0001-7124-7164
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1164-8403
2019 (English)In: TOWARDS HUMAN SCALE CITIES - OPEN AND HAPPY / [ed] Tuuli Toivonen, Karst Geurs, Elias Willberg, Helsinki: Department of Geosciences and Geography, University of Helsinki , 2019Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Travel surveys has been used for decades to observe the patterns, locations, and choices, which travellers chose and do during the given observed period. This information can be utilized as background for informed planning decisions. Despite the progress in the travel survey technologies, the applications mostly focus on more traditional travel parameters. With programmable smart watches now, we can also collect real time data that is not solely pertaining to position and travel mode choices, but also to users’ biometric data. Such an application would open another level of possibilities in dynamically integrating land use and transport planning with public health research.

Utilising a smart watch platform, we are aiming to develop a tool that will collect biometric data, in combination with spatial context, such as position, spatial features and objects in the built environment, and by utilizing machine learning algorithms, try to detect how travellers are affected by their choice of transport mode, the built environment in general as well as how the public transport is operated.

Early testing reveals the possibility to find correlations between heart rate and position, which in turn could reveal the effect of spatial and transportation elements on the traveller. By targeting widely available hardware, the scalability for this tool is virtually endless, making it possible to collect large amounts of data and utilizing machine learning algorithms to analyse it.

Place, publisher, year, edition, pages
Helsinki: Department of Geosciences and Geography, University of Helsinki , 2019.
Keywords [en]
Automated data collection, Biometric data, Built environment, Implicit interaction, Position data
National Category
Transport Systems and Logistics Interaction Technologies Social Sciences Interdisciplinary Human Computer Interaction
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-254183ISBN: 978-951-51-4922-0 (print)ISBN: 978-951-51-4921-3 (electronic)OAI: oai:DiVA.org:kth-254183DiVA, id: diva2:1328539
Conference
15th biennial NECTAR conference University of Helsinki, Finland 5-7 June 2019
Note

QC 20190823

Available from: 2019-06-21 Created: 2019-06-21 Last updated: 2019-08-23Bibliographically approved

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Using Biometric Data to Understand Travellers(216 kB)5 downloads
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Type fulltextMimetype application/pdf

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