融合卫星图像与道路网络数据，提升交通事故预测精度。
Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation
- 构建多模态数据集，融合遥感影像与道路结构特征。
- 模型融合视觉与图结构信息，预测准确率提升至90.1%。
- 发现降雨、高速路和季节变化显著影响事故率，适合交通规划者参考。
本研究利用道路网络数据与对齐于路网节点的卫星图像分析交通事故模式。以往事故预测主要依赖道路网络结构特征，忽视了路面及周边环境的物理与环境信息。本文构建了一个覆盖美国六个州的大规模多模态数据集，包含来自官方来源的九百万条交通事故记录，以及每个路网节点对应的一百万张高分辨率卫星图像。每个节点还标注了气象统计、道路类型（如住宅区与高速公路）等特征，每条边标注了交通量信息（即平均年日交通量，AADT）。基于该数据集，我们全面评估了融合视觉与图嵌入的多模态学习方法。结果表明，融合两种模态可显著提升预测性能，平均AUROC达90.1%，较仅使用图结构的GNN模型提升3.7%。利用优化后的嵌入表示，我们采用匹配估计器进行因果分析，发现经调整后，降水增多导致事故率上升24%，高速路（如高速公路）事故率上升22%，季节性因素使事故率上升29%。消融实验确认卫星图像特征对准确预测至关重要。
原文摘要 · Abstract (English)
We consider analyzing traffic accident patterns using both road network data and satellite images aligned to road graph nodes. Previous work for predicting accident occurrences relies primarily on road network structural features while overlooking physical and environmental information from the road surface and its surroundings. In this work, we construct a large multimodal dataset spanning six U.S. states, containing nine million traffic accident records from official sources, and one million high-resolution satellite images for each node of the road network. Additionally, every node is annotated with features such as the region's weather statistics and road type (e.g., residential vs. motorway), and each edge is annotated with traffic volume information (i.e., Average Annual Daily Traffic). Utilizing this dataset, we conduct a comprehensive evaluation of multimodal learning methods that integrate both visual and network embeddings. Our findings show that integrating both data modalities improves prediction accuracy, achieving an average AUROC of $90.1\%$, a $3.7\%$ gain over graph neural network models that use only graph structures. With the improved embeddings, we conduct a causal analysis using a matching estimator to identify the key factors influencing traffic accidents. We find that accident rates rise by $24\%$ under higher precipitation, by $22\%$ on higher-speed roads such as motorways, and by $29\%$ due to seasonal patterns, after adjusting for other confounding factors. Ablation studies confirm that satellite imagery features are essential for achieving accurate prediction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。