Current methods for detecting outliers in traffic streaming data often struggle to capture real-time dynamic changes in traffic conditions and differentiate between genuine changes and anomalies. This study proposes a novel approach to outlier detection in traffic streaming data that effectively addresses stochasticity and uncertainty in observations. The proposed method utilizes Stochastic Differential Equations (SDEs) and Gaussian Process Regression (GPR). By employing SDEs, we can capture drift and diffusion estimates in traffic streaming data, providing a more comprehensive modeling of the data generation process. Integrating GPR allows precise Bayesian posterior inferences for outlier detection within the SDE framework. To improve practicality, we introduce a flexible threshold-setting mechanism using statistical testing to control the false positive rate. This adaptability helps strike a balance between model fitting and complexity in outlier detection. Compared to traditional SDE-based methods, our SDE-GPR outlier detection method demonstrates enhanced robustness and better adaptability to the complexities of traffic systems. This is evidenced through an empirical study using time series data collected in California, USA. Overall, this study introduces a more advanced and accurate approach to outlier detection in traffic streaming data, paving the way for improved real-time traffic condition monitoring and management.

中文翻译：

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使用高斯过程回归辅助随机微分方程的智能出行交通流异常值检测


当前检测流量流数据中异常值的方法通常难以捕获流量状况的实时动态变化，并区分真正的变化和异常。本研究提出了一种在流量流数据中进行异常值检测的新方法，可有效解决观测中的随机性和不确定性。所提出的方法利用随机微分方程 （SDE） 和高斯过程回归 （GPR）。通过使用 SDE，我们可以捕获流量流数据中的漂移和扩散估计值，从而为数据生成过程提供更全面的建模。集成 GPR 允许在 SDE 框架内进行精确的贝叶斯后验推断以进行异常值检测。为了提高实用性，我们引入了一种灵活的阈值设置机制，使用统计测试来控制假阳性率。这种适应性有助于在模型拟合和异常值检测的复杂性之间取得平衡。与传统的基于 SDE 的方法相比，我们的 SDE-GPR 异常值检测方法表现出更强的稳健性和对交通系统复杂性的更好适应性。使用在美国加利福尼亚州收集的时间序列数据进行的一项实证研究证明了这一点。总体而言，本研究引入了一种更先进、更准确的交通流数据异常值检测方法，为改进实时交通状况监控和管理铺平了道路。