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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
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    References listed on IDEAS

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    1. Chen Xie & Dexin Yu & Xiaoyu Zheng & Zhuorui Wang & Zhongtai Jiang, 2021. "Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-21, November.
    2. Wong, Stephen D PhD & Broader, Jacquelyn C & Walker, Joan L PhD & Shaheen, Susan A PhD, 2022. "Understanding California wildfire evacuee behavior and joint choice making," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4fm7d34j, Institute of Transportation Studies, UC Berkeley.
    3. Sarah McCaffrey & Robyn Wilson & Avishek Konar, 2018. "Should I Stay or Should I Go Now? Or Should I Wait and See? Influences on Wildfire Evacuation Decisions," Risk Analysis, John Wiley & Sons, vol. 38(7), pages 1390-1404, July.
    4. Zhang, Xiaojian & Zhou, Zhengze & Xu, Yiming & Zhao, Xilei, 2024. "Analyzing spatial heterogeneity of ridesourcing usage determinants using explainable machine learning," Journal of Transport Geography, Elsevier, vol. 114(C).
    5. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    6. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
    7. Mozumder, Pallab & Raheem, Nejem & Talberth, John & Berrens, Robert P., 2008. "Investigating intended evacuation from wildfires in the wildland-urban interface: Application of a bivariate probit model," Forest Policy and Economics, Elsevier, vol. 10(6), pages 415-423, August.
    8. Yihan Wu & Todd A. Mooring & Marianna Linz, 2021. "Policy and weather influences on mobility during the early US COVID-19 pandemic," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(22), pages 2018185118-, June.
    9. Adam Pel & Michiel Bliemer & Serge Hoogendoorn, 2012. "A review on travel behaviour modelling in dynamic traffic simulation models for evacuations," Transportation, Springer, vol. 39(1), pages 97-123, January.
    10. Michael K. Lindell & Ronald W. Perry, 2012. "The Protective Action Decision Model: Theoretical Modifications and Additional Evidence," Risk Analysis, John Wiley & Sons, vol. 32(4), pages 616-632, April.
    11. Jinjun Tang & Fan Gao & Fang Liu & Wenhui Zhang & Yong Qi, 2019. "Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM," Sustainability, MDPI, vol. 11(19), pages 1-19, October.
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