Graph Neural Networks for Travel Distance Estimation and Route Recommendation Under Probabilistic Hazards
This work addresses emergency planning and management by providing fast route recommendations for evacuation during extreme events like hurricanes, though it is incremental as it applies GNNs to an existing problem.
The paper tackles the problem of estimating shortest travel distances and recommending routes under probabilistic hazards by proposing a graph neural network (GNN) framework, which demonstrates accuracy and computational efficiency in synthetic and real-world flood risk evacuation scenarios.
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.