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Sparse Traffic Accident Risk Forecasting With Spatial-Temporal Knowledge Graphs
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DOI：10.1109/tkde.2026.3706345.png)
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Abstract
En 中文
Accurate prediction of traffic accident risk at both the road segment and taxi zone granularities is essential for modern intelligent transportation systems. Such predictions offer early warnings to travelers and transportation authorities, contributing to safer mobility. However, achieving accurate traffic accident risk prediction faces two major challenges. Firstly, traffic accident risk is affected by multi-source, entangled and dynamic factors. Accurate accident risk prediction requires a deep understanding of the interactions among these factors, while existing methods struggle to address this task effectively. Secondly, the sparsity of traffic accident data leads to the zero-inflation issue, causing models to appear effective on risk regression metrics while failing to accurately identify high-risk regions. To address these challenges, we propose STKGRisk, a novel model that simultaneously predicts traffic accident risk for both road segments and taxi zones. Specifically, we construct the first spatial-temporal knowledge graph for traffic accident risk analysis and design a diachronic embedding module to capture the high-order, dynamic interactions between multi-source factors and traffic accidents. Building upon these embeddings, we develop a spatial-temporal graph network encoder to capture the spatial-temporal correlations of accident risk for two granularities from multi-level and multi-view perspectives. To tackle the zero-inflation issue, we design a Zero-Inflated Mixture Poisson decoder to learn the occurrence patterns of sparse accident risk data. Extensive experiments on three real-world traffic accident datasets demonstrate that STKGRisk outperforms existing models. STKGRisk almost achieves the best predictive performance on both the segment- and zone-granularity accident risk prediction tasks, and excels particularly in the identification of high-risk areas.
Keywords:
Spatial-temporal data mining
traffic accident risk prediction
spatial-temporal knowledge graph
zero-inflation mixture poisson
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