用图神经网络快速估算城市灾后最短通行时间与路线。
Graph Neural Networks for Travel Distance Estimation and Route Recommendation Under Probabilistic Hazards
- 用GNN近似单源最短路径，替代传统耗时的Dijkstra算法。
- 在合成图和真实沿海城市洪水案例中，精度高且计算速度快。
- 适合应急规划、灾害疏散路线推荐场景使用。
在极端事件期间或之后，量化城市或区域间最短通行时间并提供路线推荐，有助于评估交通网络状态。传统方法如Dijkstra算法虽能求出最短路径与距离，但在大规模网络上计算成本过高。本文提出一种基于图神经网络（GNN）的新框架，可快速近似单源最短距离，并后续预测最短路径。我们在不同规模的合成图上进行了多组实验，验证了该模型的可行性与计算效率。在真实世界案例中，将该方法应用于沿海城市洪涝风险分析，计算飓风期间向公共避难所撤离的延误情况。结果表明，GNN模型兼具高精度与高效性，具备在应急规划与管理中实际应用的潜力。
原文摘要 · Abstract (English)
Estimating the shortest travel time and providing route recommendation between different locations in a city or region can quantitatively measure the conditions of the transportation network during or after extreme events. One common approach is to use Dijkstra's Algorithm, which produces the shortest path as well as the shortest distance. However, this option is computationally expensive when applied to large-scale networks. This paper proposes a novel fast framework based on graph neural networks (GNNs) which approximate the single-source shortest distance between pairs of locations, and predict the single-source shortest path subsequently. We conduct multiple experiments on synthetic graphs of different size to demonstrate the feasibility and computational efficiency of the proposed model. In real-world case studies, we also applied the proposed method of flood risk analysis of coastal urban areas to calculate delays in evacuation to public shelters during hurricanes. The results indicate the accuracy and computational efficiency of the GNN model, and its potential for effective implementation in emergency planning and management.
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