  Traffic accident forecasting is a significant problem for transportation
management and public safety. However, this problem is challenging due to the
spatial heterogeneity of the environment and the sparsity of accidents in space
and time. The occurrence of traffic accidents is affected by complex
dependencies among spatial and temporal features. Recent traffic accident
prediction methods have attempted to use deep learning models to improve
accuracy. However, most of these methods either focus on small-scale and
homogeneous areas such as populous cities or simply use sliding-window-based
ensemble methods, which are inadequate to handle heterogeneity in large
regions. To address these limitations, this paper proposes a novel Hierarchical
Knowledge Transfer Network (HintNet) model to better capture irregular
heterogeneity patterns. HintNet performs a multi-level spatial partitioning to
separate sub-regions with different risks and learns a deep network model for
each level using spatio-temporal and graph convolutions. Through knowledge
transfer across levels, HintNet archives both higher accuracy and higher
training efficiency. Extensive experiments on a real-world accident dataset
from the state of Iowa demonstrate that HintNet outperforms the
state-of-the-art methods on spatially heterogeneous and large-scale areas.
