利用高斯过程回归辅助随机微分方程检测智能交通流量异常值

IF 8.3 1区 工程技术 Q1 ECONOMICS
Qixiu Cheng , Guiqi Dai , Bowei Ru , Zhiyuan Liu , Wei Ma , Hongzhe Liu , Ziyuan Gu
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引用次数: 0

摘要

目前在交通流数据中检测异常值的方法往往难以捕捉交通状况的实时动态变化,也难以区分真正的变化和异常。本研究提出了一种在交通流数据中检测异常值的新方法,可有效解决观测中的随机性和不确定性问题。所提出的方法利用了随机微分方程 (SDE) 和高斯过程回归 (GPR)。通过使用 SDE,我们可以捕捉到交通流数据中的漂移和扩散估计,为数据生成过程提供更全面的建模。整合 GPR 可以在 SDE 框架内进行精确的贝叶斯后验推断,从而进行离群点检测。为了提高实用性,我们引入了灵活的阈值设置机制,利用统计测试来控制误报率。这种适应性有助于在离群点检测的模型拟合和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 离群点检测方法显示出更强的鲁棒性和对复杂交通系统的更好适应性。通过使用在美国加利福尼亚州收集的时间序列数据进行实证研究,证明了这一点。总之,本研究为交通流数据中的离群点检测引入了一种更先进、更准确的方法,为改善实时交通状况监控和管理铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Traffic Flow Outlier Detection for Smart Mobility Using Gaussian Process Regression Assisted Stochastic Differential Equations
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.
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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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