Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding
Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding
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DOI：
10.1016/j.compenvurbsys.2022.101870
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Faxi Yuan;Yuanchang Xu;Qingchun Li;A. Mostafavi
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文献类型：
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作者：
Faxi Yuan;Yuanchang Xu;Qingchun Li;A. Mostafavi
本研究的目的是通过使用细粒度交通数据的深度学习框架，根据路段自身和相邻路段的当前状况来预测路段近期的洪水状况。对道路网络状态进行态势感知的预测洪水监测对于支持危机应对活动（例如评估无法进入医院和避难所）发挥着关键作用。现有的与路段层面的路网洪水状况近期预测相关的研究缺失。本研究利用与路段相关的细粒度交通速度数据，设计并实现了三个时空图卷积网络 (STGCN) 模型，以预测 2017 年美国德克萨斯州哈里斯县飓风哈维期间路段洪水事件期间的路网状态。模型 1 由两个时空块组成，考虑了路段之间的相邻性和距离，而模型 2 包含一个额外的高程块来考虑路段之间的高程差异。模型3包括三个块，用于考虑路段之间的邻接性以及距离和高程差的乘积。该分析测试了 STGCN 模型并评估了它们的预测性能。我们的结果表明，模型 1 和模型 2 在预测不久的将来（例如 2-4 小时）路网洪水状态方面具有可靠且准确的性能，模型精度和召回值分别大于 98% 和 96%。通过对洪水中可靠的路网状态进行预测，所提出的模型可以使受影响的社区避免被洪水淹没的道路，并帮助应急管理机构实施疏散和救援资源交付计划
The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans