  Traffic accidents pose a significant risk to human health and property
safety. Therefore, to prevent traffic accidents, predicting their risks has
garnered growing interest. We argue that a desired prediction solution should
demonstrate resilience to the complexity of traffic accidents. In particular,
it should adequately consider the regional background, accurately capture both
spatial proximity and semantic similarity, and effectively address the sparsity
of traffic accidents. However, these factors are often overlooked or difficult
to incorporate. In this paper, we propose a novel multi-granularity
hierarchical spatio-temporal network. Initially, we innovate by incorporating
remote sensing data, facilitating the creation of hierarchical
multi-granularity structure and the comprehension of regional background. We
construct multiple high-level risk prediction tasks to enhance model's ability
to cope with sparsity. Subsequently, to capture both spatial proximity and
semantic similarity, region feature and multi-view graph undergo encoding
processes to distill effective representations. Additionally, we propose
message passing and adaptive temporal attention module that bridges different
granularities and dynamically captures time correlations inherent in traffic
accident patterns. At last, a multivariate hierarchical loss function is
devised considering the complexity of the prediction purpose. Extensive
experiments on two real datasets verify the superiority of our model against
the state-of-the-art methods.
