Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires

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dc.citation.volume190
dc.contributor.authorZhang X
dc.contributor.authorZhao X
dc.contributor.authorXu Y
dc.contributor.authorNilsson D
dc.contributor.authorLovreglio R
dc.date.accessioned2024-10-20T20:30:08Z
dc.date.available2024-10-20T20:30:08Z
dc.date.issued2024-09-10
dc.description.abstractNatural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. To tackle this research gap, the study develops a new methodological framework for modeling highly granular spatiotemporal trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested using a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. Our finding suggests that the most important model components of SA-MGCRN are weekend indicator, population change, evacuation order/warning information, and proximity to fire, which are consistent with behavioral theories and empirical findings. SA-MGCRN can be directly used in future wildfire events to assist real-time decision-making and emergency management.
dc.description.confidentialfalse
dc.edition.editionDecember 2024
dc.identifier.citationZhang X, Zhao X, Xu Y, Nilsson D, Lovreglio R. (2024). Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires. Transportation Research Part A: Policy and Practice. 190.
dc.identifier.doi10.1016/j.tra.2024.104242
dc.identifier.eissn1879-2375
dc.identifier.elements-typejournal-article
dc.identifier.issn0965-8564
dc.identifier.number104242
dc.identifier.piiS0965856424002908
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71767
dc.languageEnglish
dc.publisherElsevier B.V.
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0965856424002908
dc.relation.isPartOfTransportation Research Part A: Policy and Practice
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleSituational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires
dc.typeJournal article
pubs.elements-id491553
pubs.organisational-groupCollege of Health
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