  Traffic accident prediction is a crucial task in the mobility domain.
State-of-the-art accident prediction approaches are based on static and uniform
grid-based geospatial aggregations, limiting their capability for fine-grained
predictions. This property becomes particularly problematic in more complex
regions such as city centers. In such regions, a grid cell can contain
subregions with different properties; furthermore, an actual accident-prone
region can be split across grid cells arbitrarily. This paper proposes Adaptive
Clustering Accident Prediction (ACAP) - a novel accident prediction method
based on a grid growing algorithm. ACAP applies adaptive clustering to the
observed geospatial accident distribution and performs embeddings of temporal,
accident-related, and regional features to increase prediction accuracy. We
demonstrate the effectiveness of the proposed ACAP method using open real-world
accident datasets from three cities in Germany. We demonstrate that ACAP
improves the accident prediction performance for complex regions by 2-3 percent
points in F1-score by adapting the geospatial aggregation to the distribution
of the underlying spatio-temporal events. Our grid growing approach outperforms
the clustering-based baselines by four percent points in terms of F1-score on
average.
