Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Forecasting incident occurrences (e.g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management. However, such events are by nature rare and sparse, which causes the label imbalance problem and inferior performance of models relying on data sufficiency. The existing studies circumvent, instead of truly solving, this issue by defining the incident prediction problem in a coarse-grained temporal (e.g. daily) setting, which leaves the proposed models unrobust to fine-grained dynamics and trivial for the real-world decision making. In this paper, we tackle the temporally fine-grained incident prediction problem in a sparse setting by explicitly exploiting the behind-thescene chainlike triggering mechanism. Moreover, this chain effect roots in multiple domains (i.e. spatial, categorical), which further entangles with the temporal dimension and happens to be timevariant. To be specific, we propose a novel deep learning framework, namely Spatio-Temporal-Categorical Graph Neural Networks (STCGNN), to handle the multidimensional and dynamic chain effect for performing fine-grained multi-incident co-prediction. Extensive experiments on three real-world city-level incident datasets verify the insightfulness of our perspective and effectiveness of the proposed model.
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