<ul><li>Traffic accident forecasting is an important task for intelligent transportation management and emergency response systems.</li><li>Existing data-driven methods mostly focus on studying homogeneous areas with limited size and fail to handle the heterogeneous accident patterns over space at different scales.</li><li>This paper proposes a novel Learning-Integrated Space Partition Framework (LISA) that simultaneously learns partitions while training models, guided by prediction accuracy.</li><li>Experimental results using real-world datasets show that LISA captures underlying heterogeneous patterns and improves baseline networks by an average of 13.0%.</li></ul>

LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data

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