Guan Xiangyang, Chen Cynthia
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195 USA.
Transp Res E Logist Transp Rev. 2021 Aug;152. doi: 10.1016/j.tre.2021.102381. Epub 2021 Jun 19.
Timely and accurate forecast of evacuation demand is key for emergency responders to plan and organize effective evacuation efforts during a disaster. The state-of-the-art in evacuation demand forecasting includes behavior-based models and dynamic flow-based models. Both lines of work have critical limitations: behavioral models are static, meaning that they cannot adjust model predictions by utilizing field observation in real time as the disaster is unfolding; and the flow-based models often have relatively short prediction windows ranging from minutes to hours. Consequently, both types of models fall short of being able to predict sudden changes (e.g., a surge or abrupt drop) of evacuation demand in advance. This paper develops a behaviorally-integrated individual-level state-transition model for online predictions of evacuation demand. On a daily basis, the model takes in observed evacuation data and updates its forecasted evacuation demand for the future. An individual-level survival model formulation is innovatively devised for the state-transition model to account for history-dependent transition probabilities and allow individual heterogeneity. A Bayesian updating approach is employed to assimilate observed evacuation data in real time. To enable a longer-term perspective on how evacuation demand may evolve over time so that rapid surges or drops in demand can be predicted days in advance, the model integrates insights from existing behavioral curves (either from past disasters or simply expert opinions). Using a likelihood-based approach, the state-transition model integrates the future trends of evacuation demand informed by the behavioral curve when updating its forecasts. Theoretical proof is also provided showing that the likelihood function guarantees a unique global solution to the state-transition model. The proposed model is tested in six scenarios using mobile app-based data for Hurricane Harvey that hit the US in 2017. The results demonstrate overall robustness of the proposed model: in all six scenarios, the model is able to predict accurately the occurrence of the rapid surges or drops in evacuation demand at least two days ahead. The current study contributes to the field of evacuation modeling by integrating the two lines of work (behavior-based and flow-based models) using mobile app-based data.
及时准确地预测疏散需求是应急响应人员在灾难期间规划和组织有效疏散工作的关键。疏散需求预测的最新技术包括基于行为的模型和基于动态流量的模型。这两种方法都存在严重局限性：行为模型是静态的，这意味着它们无法在灾难发生时利用现场观测实时调整模型预测；而基于流量的模型预测窗口通常较短，从几分钟到几小时不等。因此，这两种模型都无法提前预测疏散需求的突然变化（例如，激增或急剧下降）。本文开发了一种行为整合的个体层面状态转换模型，用于在线预测疏散需求。该模型每天接收观测到的疏散数据，并更新其对未来疏散需求的预测。为状态转换模型创新性地设计了个体层面的生存模型公式，以考虑历史依赖的转换概率并允许个体异质性。采用贝叶斯更新方法实时吸收观测到的疏散数据。为了能够从更长期的角度了解疏散需求如何随时间演变，从而提前数天预测需求的快速激增或下降，该模型整合了现有行为曲线（来自过去灾难或仅是专家意见）的见解。状态转换模型在更新预测时，使用基于似然的方法整合行为曲线所反映的疏散需求未来趋势。还提供了理论证明，表明似然函数保证了状态转换模型有唯一的全局解。使用2017年袭击美国的飓风哈维的基于移动应用程序的数据，在六个场景中对所提出的模型进行了测试。结果证明了所提出模型的整体稳健性：在所有六个场景中，该模型能够至少提前两天准确预测疏散需求的快速激增或下降的发生。当前的研究通过使用基于移动应用程序的数据整合两种方法（基于行为的模型和基于流量的模型），为疏散建模领域做出了贡献。
