Graph Neural Networks in Emergency Management: A Case Study on Fire Call Assignment

This paper introduces a Graph Neural Network (GNN) approach to tackle the problem of emergency fire call assignment. The dataset utilized comprises emergency fire calls received by the 911 service in San Francisco. Each call is processed and assigned to a specific fire station. To capture the geographical nuances, we preprocess the dataset into a bipartite graph representation. The right nodes of the graph represent the calls with their geographical coordinates (latitude and longitude), while the left nodes signify the stations and their current occupancy. The graph edges represent the distances between calls and stations. Our GNN model predicts the optimal assignment of calls to stations based on unit availability. Instead of the conventional method of routing calls to the nearest station, our model bypasses this system to directly forecast the station with available units for each call. Through rigorous experimentation, we showcase the superiority of our method in enhancing the call assignment process. By leveraging the intrinsic graph structure, the GNN model taps into the rich information within the dataset. This study advances the field of emergency management by offering a groundbreaking optimization framework that can markedly improve resource allocation in emergency response systems.