Abstract：
Traffic accidents have emerged as one of the most public health safety matters, raising concerns from both the public and urban administrators. The ability to accurately predict traffic accident not only supports the governmental decision-making in advance but also enhances public confidence in safety measures. However, the efficacy of traditional spatio-temporal prediction models are compromised by the skewed distributions and sparse labeling of accident data. To this end, we propose a Sparse Spatio-Temporal Dynamic Hypergraph Learning (SST-DHL) framework that captures higher-order dependencies in sparse traffic accidents by combining hypergraph learning and self-supervised learning. The SST-DHL model incorporates a multi-view spatiotemporal convolution block to capture local correlations and semantics of traffic accidents, a cross-regional dynamic hypergraph learning model to identify global spatiotemporal dependencies, and a two-supervised self-learning paradigm to capture both local and global spatiotemporal patterns. Through experimentation on New York City and London accident datasets, we demonstrate that our proposed SST-DHL exhibits significant improvements compared to optimal baseline models at different sparsity levels. Additionally, it offers enhanced interpretability of results by elucidating complex spatio-temporal dependencies among various traffic accident instances. Our study demonstrates the effectiveness of the SST-DHL framework in accurately predicting traffic accidents, thereby enhancing public safety and trust.
摘要：
交通事故已成为最公共卫生安全问题之一，引起公众和城市管理者的关注。准确预测交通事故的能力不仅支持政府的提前决策，而且增强了公众对安全措施的信心。然而,传统的时空预测模型的有效性受到事故数据的偏斜分布和稀疏标记的影响。为此,我们提出了一个稀疏时空动态超图学习（SST-DHL）框架，该框架通过结合超图学习和自监督学习来捕获稀疏交通事故中的高阶依赖关系。SST-DHL模型包含多视图时空卷积块，以捕获交通事故的局部相关性和语义，识别全球时空依赖关系的跨区域动态超图学习模型，和双监督的自我学习范式，以捕获局部和全局时空模式。通过对纽约市和伦敦事故数据集的实验，我们证明了我们提出的SST-DHL在不同稀疏度水平下与最佳基线模型相比具有显着改善。此外,它通过阐明各种交通事故实例之间复杂的时空依赖性，提高了结果的可解释性。我们的研究证明了SST-DHL框架在准确预测交通事故方面的有效性，从而增强公共安全和信任。
