用于野火期间旅行需求预测的情境感知多图卷积递归网络(SA-MGCRN)

IF 6.3 1区 工程技术 Q1 ECONOMICS
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引用次数: 0

摘要

野火等自然灾害对全球社区构成了重大威胁。野火疏散期间旅行需求的实时预测对于应急管理人员和交通规划人员及时做出更明智的决策至关重要。然而,很少有研究关注大规模紧急疏散中的准确旅行需求预测。针对这一研究空白,本研究开发了一种新的方法框架,通过使用(a)移动设备生成的大规模 GPS 数据和(b)最先进的人工智能技术,对野火疏散中高粒度时空旅行生成进行建模。基于从 GPS 数据推断出的出行需求,我们开发了一种新的深度学习模型,即 "情境感知多图卷积递归网络"(SA-MGCRN),并制定了模型更新方案,以实现对野火疏散期间出行需求的实时预测。所提出的方法框架通过实际案例研究进行了测试:2019 年加利福尼亚州索诺玛县的金卡德火灾。结果表明,SA-MGCRN 在预测性能方面明显优于所有选定的最先进基准。我们的研究结果表明,SA-MGCRN 最重要的模型组件是周末指标、人口变化、疏散命令/警告信息和距离火灾的远近,这与行为理论和实证研究结果是一致的。SA-MGCRN可直接用于未来的野火事件,协助实时决策和应急管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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, 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.

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来源期刊
CiteScore
13.20
自引率
7.80%
发文量
257
审稿时长
9.8 months
期刊介绍: Transportation Research: Part A contains papers of general interest in all passenger and freight transportation modes: policy analysis, formulation and evaluation; planning; interaction with the political, socioeconomic and physical environment; design, management and evaluation of transportation systems. Topics are approached from any discipline or perspective: economics, engineering, sociology, psychology, etc. Case studies, survey and expository papers are included, as are articles which contribute to unification of the field, or to an understanding of the comparative aspects of different systems. Papers which assess the scope for technological innovation within a social or political framework are also published. The journal is international, and places equal emphasis on the problems of industrialized and non-industrialized regions. Part A''s aims and scope are complementary to Transportation Research Part B: Methodological, Part C: Emerging Technologies and Part D: Transport and Environment. Part E: Logistics and Transportation Review. Part F: Traffic Psychology and Behaviour. The complete set forms the most cohesive and comprehensive reference of current research in transportation science.
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