STContext：用于开发上下文感知时空人群移动预测模型的多面数据集
STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models
AI总结：
针对现有开放人群流量数据集上下文特征不足的问题，本文构建覆盖五类场景、含十类上下文特征的STContext数据集，并提出统一上下文建模工作流程，通过实验总结有效建模指南。
摘要：
在智慧城市中，上下文感知的时空人群流量预测（STCFP）模型利用天气等上下文特征来识别异常的人群移动模式并提高预测精度。然而，由于不同论文对上下文特征的使用不一致，如何纳入上下文特征的最佳实践仍不明确。开发一个包含丰富上下文特征类型和STCFP场景的多面数据集，对于建立有原则的上下文建模范式至关重要。现有开放人群流量数据集缺乏足够广泛的上下文特征，因此迫切需要构建一个多面数据集以填补这些研究空白。为此，我们创建了STContext，这是一个用于开发上下文感知STCFP模型的多面数据集。具体而言，STContext提供覆盖五个STCFP场景的九个时空数据集，并包含十类上下文特征，包括天气、空气质量指数、节假日、兴趣点、道路网络等。此外，我们提出了一种将上下文特征纳入深度STCFP方法的统一工作流程，步骤包括特征转换、依赖建模、表示融合和训练策略。通过大量实验，我们获得了若干有效上下文建模的有用指南以及对未来研究的洞见。STContext已在https://github.com/Liyue-Chen/STContext开源。
英文摘要：
In smart cities, context-aware spatio-temporal crowd flow prediction (STCFP) models leverage contextual features (e.g., weather) to identify unusual crowd mobility patterns and enhance prediction accuracy. However, the best practice for incorporating contextual features remains unclear due to inconsistent usage of contextual features in different papers. Developing a multifaceted dataset with rich types of contextual features and STCFP scenarios is crucial for establishing a principled context modeling paradigm. Existing open crowd flow datasets lack an adequate range of contextual features, which poses an urgent requirement to build a multifaceted dataset to fill these research gaps. To this end, we create STContext, a multifaceted dataset for developing context-aware STCFP models. Specifically, STContext provides nine spatio-temporal datasets across five STCFP scenarios and includes ten contextual features, including weather, air quality index, holidays, points of interest, road networks, etc. Besides, we propose a unified workflow for incorporating contextual features into deep STCFP methods, with steps including feature transformation, dependency modeling, representation fusion, and training strategies. Through extensive experiments, we have obtained several useful guidelines for effective context modeling and insights for future research. The STContext is open-sourced at https://github.com/Liyue-Chen/STContext.
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。
