  Real-time traffic accident forecasting is increasingly important for public
safety and urban management (e.g., real-time safe route planning and emergency
response deployment). Previous works on accident forecasting are often
performed on hour levels, utilizing existed neural networks with static
region-wise correlations taken into account. However, it is still challenging
when the granularity of forecasting step improves as the highly dynamic nature
of road network and inherent rareness of accident records in one training
sample, which leads to biased results and zero-inflated issue. In this work, we
propose a novel framework RiskOracle, to improve the prediction granularity to
minute levels. Specifically, we first transform the zero-risk values in labels
to fit the training network. Then, we propose the Differential Time-varying
Graph neural network (DTGN) to capture the immediate changes of traffic status
and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and
region selection schemes to highlight citywide most-likely accident subregions,
bridging the gap between biased risk values and sporadic accident distribution.
Extensive experiments on two real-world datasets demonstrate the effectiveness
and scalability of our RiskOracle framework.
