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

Natural 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, the authors 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. The authors' 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.

• Record URL:
  • https://doi.org/10.1016/j.tra.2024.104242
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0965856424002908
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • © 2024 The Author(s). Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Xiaojian
  • 0000-0002-1414-8204
  • Zhao, Xilei
  • 0000-0002-7903-4806
  • Xu, Yiming
  • 0000-0002-2983-1751
  • Nilsson, Daniel
  • Lovreglio, Ruggiero
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 104242
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 190
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Decision making; Emergency management; Evacuation; Global Positioning System; Machine learning; Traffic forecasting; Wildfires
• Geographic Terms: Sonoma County (California)
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01932402
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 30 2024 5:21PM