  Traffic accidents present substantial challenges to human safety and
socio-economic development in urban areas. Developing a reliable and
responsible traffic accident prediction model is crucial to addressing growing
public safety concerns and enhancing the safety of urban mobility systems.
Traditional methods face limitations at fine spatiotemporal scales due to the
sporadic nature of highrisk accidents and the predominance of non-accident
characteristics. Furthermore, while most current models show promising
occurrence prediction, they overlook the uncertainties arising from the
inherent nature of accidents, and then fail to adequately map the hierarchical
ranking of accident risk values for more precise insights. To address these
issues, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural
Network STZITDGNN -- the first uncertainty-aware probabilistic graph deep
learning model in roadlevel traffic accident prediction for multisteps. This
model integrates the interpretability of the statistical Tweedie family model
and the expressive power of graph neural networks. Its decoder innovatively
employs a compound Tweedie model,a Poisson distribution to model the frequency
of accident occurrences and a Gamma distribution to assess injury severity,
supplemented by a zeroinflated component to effectively identify exessive
nonincident instances. Empirical tests using realworld traffic data from
London, UK, demonstrate that the STZITDGNN surpasses other baseline models
across multiple benchmarks and metrics, including accident risk value
prediction, uncertainty minimisation, non-accident road identification and
accident occurrence accuracy. Our study demonstrates that STZTIDGNN can
effectively inform targeted road monitoring, thereby improving urban road
safety strategies.
