SMA-Hyper：用于交通事故预测的时空多视图融合超图学习
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction
AI总结：
提出SMA-Hyper模型，通过双重自适应时空图学习、对比学习和注意力机制融合多视图数据，有效解决交通事故预测中的数据稀疏与高阶依赖问题，显著优于基线模型。
摘要：
预测交通事故是可持续城市管理的关键，需要有效应对城市动态且复杂的时空特征。当前的数据驱动模型通常难以处理数据稀疏性问题，且往往忽视了多元城市数据源的整合及其内部的高阶依赖关系。此外，它们频繁依赖预定义的拓扑结构或权重，限制了其在时空预测中的适应性。为了解决这些问题，我们提出了时空多视图自适应超图学习（SMA-Hyper）模型，这是一个专为交通事故预测设计的动态深度学习框架。基于先前的研究，该创新模型结合了双重自适应时空图学习机制，通过超图实现高阶跨区域学习，并动态适应不断变化的城市数据。它还利用对比学习来增强稀疏数据集中的全局和局部数据表示，并采用先进的注意力机制融合事故数据和城市功能特征的多个视图，从而丰富对风险因素的上下文理解。在伦敦交通事故数据集上的广泛测试表明，SMA-Hyper模型在各种时间范围和多步输出中均显著优于基线模型，证实了其多视图融合和自适应学习策略的有效性。结果的可解释性进一步强调了其通过利用复杂的时空城市数据来改善城市交通管理和安全的潜力，提供了一个可适应各种城市环境的可扩展框架。
英文摘要：
Predicting traffic accidents is the key to sustainable city management, which requires effective address of the dynamic and complex spatiotemporal characteristics of cities. Current data-driven models often struggle with data sparsity and typically overlook the integration of diverse urban data sources and the high-order dependencies within them. Additionally, they frequently rely on predefined topologies or weights, limiting their adaptability in spatiotemporal predictions. To address these issues, we introduce the Spatiotemporal Multiview Adaptive HyperGraph Learning (SMA-Hyper) model, a dynamic deep learning framework designed for traffic accident prediction. Building on previous research, this innovative model incorporates dual adaptive spatiotemporal graph learning mechanisms that enable high-order cross-regional learning through hypergraphs and dynamic adaptation to evolving urban data. It also utilises contrastive learning to enhance global and local data representations in sparse datasets and employs an advance attention mechanism to fuse multiple views of accident data and urban functional features, thereby enriching the contextual understanding of risk factors. Extensive testing on the London traffic accident dataset demonstrates that the SMA-Hyper model significantly outperforms baseline models across various temporal horizons and multistep outputs, affirming the effectiveness of its multiview fusion and adaptive learning strategies. The interpretability of the results further underscores its potential to improve urban traffic management and safety by leveraging complex spatiotemporal urban data, offering a scalable framework adaptable to diverse urban environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
