Are the data good enough? Spatial and temporal modeling of evacuee behavior using GPS data in a small rural community
Bibliographic Data
| ID | 22027739 |
|---|---|
| Authors | Bahareh Raei (0009-0007-0656-7349, University of Calgary), Max Kinateder (0000-0002-1669-3900, National Research Council Canada), Noureddine Bénichou (0000-0002-8878-2283, National Research Council Canada), Islam Gomaa (0000-0003-0186-6669, National Research Council Canada), Xin Wang (0000-0003-2833-6940, University of Calgary, corresponding author) |
| Year | 2025 |
| Volume | 116 |
| Pages | 105054 |
| Publication date | 2025-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Disaster Risk Reduction (JOURNAL) |
| Journal identifiers | ISSN: 2212-4209 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2024.105054 |
| OpenAlex | W4405624726 |
| Language | EN |
| References cited | 37 |
The growing frequency and intensity of wildfires pose significant challenges for Canadian communities in the Wildland-Urban Interface (WUI). This case study explores the evacuation dynamics (traffic movement) during the 2021 wildfire in Lytton, BC. Drawing upon Global Positioning System (GPS) data, the study proposes novel methodologies for departure time analysis and traffic prediction tailored to the local wildfire context. The methodology offers insights into the temporal and spatial movement patterns of residents within the community before and during the wildfire event. By employing stay point detection and temporal analysis techniques, the study quantifies evacuation behavior, shedding light on departure times and evacuation trends. In addition, the study presents a comprehensive methodology for predicting traffic dynamics on highways during wildfire evacuations beyond the case study. Leveraging GPS data and machine learning techniques, the proposed approach integrates spatial and temporal analyses with predictive modeling to forecast traffic conditions accurately. Our analysis revealed that the southbound exit roads to the highway experienced significant traffic congestion during the wildfire. Overall, this work contributes to our understanding of WUI community preparedness and evacuation by providing insights into evacuation behaviors, preferred routes, and potential traffic challenges. However, the results also clearly exposed the limitations of GPS data from smaller communities in sparsely populated areas. Further research is needed to enhance our comprehension of wildfire responses, especially within underserved communities in rural areas. This will allow for the improvement of predictive models and the creation of more efficient evacuation planning strategies. Such advancements are crucial in mitigating risks and ensuring the safety of WUI residants
Data science · Geography · Global Positioning System · Remote sensing · Spatial analysis · Telecommunications · Transport engineering · Computer Science · Engineering · Environmental Science · Human Mobility and Location-Based Analysis · Impact of Light on Environment and Health · Urban Transport and Accessibility
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Destination unknown
| Highly cited | No |
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