IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v190y2024ics0965856424002908.html
   My bibliography  Save this article

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

Author

Listed:
  • Zhang, Xiaojian
  • Zhao, Xilei
  • Xu, Yiming
  • Nilsson, Daniel
  • Lovreglio, Ruggiero

Abstract

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, 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.

Suggested Citation

  • Zhang, Xiaojian & Zhao, Xilei & Xu, Yiming & Nilsson, Daniel & Lovreglio, Ruggiero, 2024. "Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
  • Handle: RePEc:eee:transa:v:190:y:2024:i:c:s0965856424002908
    DOI: 10.1016/j.tra.2024.104242
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856424002908
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2024.104242?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:190:y:2024:i:c:s0965856424002908. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.