Dugstad, Ann‐Kristin, Berthiaume, Maxine, Ronchi, Enrico ORCID: https://orcid.org/0000-0002-2789-6359, Bénichou, Noureddine, Geoerg, Paul, Gwynne, Steve ORCID: https://orcid.org/0000-0002-2758-3897, Xie, Hui, Kubose‐Peutz, Kamryn, Kimball, Amanda and Kinateder, Max (2025) Traffic flow data collection methods during wildfire evacuation. Fire and Materials (FAM), 154:105258. ISSN 0308-0501 (Print), 1099-1018 (Online) (doi:10.1016/j.trd.2026.105258)

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Abstract

With climate change and urbanisation increasing wildfire risk in the wildland–urban interface (WUI), enhancing community evacuation preparedness is crucial. This study evaluates different data collection methods for measuring traffic flows during a community wildfire evacuation drill in Roxborough Park, Colorado, in June 2024. A multi‐method strategy was applied, including drone footage, human observers (manual and app‐assisted), automated traffic counters, questionnaires, and postcards. Methods were assessed based on cost, ease of use, data quality, privacy, and ethical considerations. Results highlight that drone footage effectively captured large‐scale traffic flow but required a large number of resources for preparation and post‐processing. Human observers provided detailed insights but might be prone to errors in high‐traffic areas. Self‐reported data (questionnaires, postcards) offered valuable behavioural insights but might be affected by response bias. Automated traffic counters provided continuous data but could not differentiate between drill‐specific and background traffic. This study provides practical guidance on selecting behavioural data collection methods for wildfire evacuation research, ultimately contributing to improved emergency planning and community resilience.

Item Type:	Article
Uncontrolled Keywords:	evacuation, human behaviour in fire, traffic flow, wildfire, wildland–urban interface, WUI
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	21 May 2026 10:42
URI:	https://gala.gre.ac.uk/id/eprint/53540

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