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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Detecting Road Traffic Events by Coupling Multiple Timeseries With a Nonparametric Bayesian Method
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Detecting Road Traffic Events by Coupling Multiple Timeseries With a Nonparametric Bayesian Method

机译：通过使用非参数贝叶斯方法耦合多个时间序列来检测道路交通事件

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摘要

Road traffic sensors provide rich multivariable datastreams about the current traffic conditions. Occasionally, there are unusual traffic events (such as accidents, jams, and severe weather) that disrupt the expected road traffic conditions. Detecting the occurrence of such events in an online and real-time manner is useful to drivers in planning their routes and in the management of the transportation infrastructure. We propose a new method for detecting traffic events that impact road traffic conditions by extending the Bayesian robust principal component analysis (RPCA) approach. Our method couples multiple traffic datastreams so that they share a certain sparse structure. This sparse structure is used to localize traffic events in space and time. The traffic datastreams are measurements of different physical quantities (e.g., traffic flow and road occupancy) by different nearby sensors. Our proposed method processes datastreams in an incremental way with small computational cost; hence, it is suitable to detect events in an online and real-time manner. We experimentally analyze the detection performance of the proposed coupled Bayesian RPCA (BRPCA) using real data from loop detectors on the Minnesota I-494. We find that our method significantly improves the detection accuracy when compared with the traditional PCA and noncoupled BRPCA.
机译：道路交通传感器提供有关当前交通状况的丰富的多变量数据流。偶尔会有不寻常的交通事件（例如事故，交通拥堵和恶劣的天气）干扰预期的道路交通状况。以在线和实时方式检测此类事件的发生，对于驾驶员规划路线和管理交通基础设施很有用。我们提出了一种通过扩展贝叶斯鲁棒主成分分析（RPCA）方法来检测影响道路交通状况的交通事件的新方法。我们的方法将多个交通数据流耦合在一起，以便它们共享某种稀疏结构。这种稀疏结构用于在空间和时间上定位交通事件。交通数据流是由附近的不同传感器对不同物理量（例如，交通流量和道路占用率）的测量。我们提出的方法以增量方式处理数据流，而计算量却很小；因此，适合以在线和实时方式检测事件。我们使用明尼苏达州I-494上环路检测器的真实数据，通过实验分析了提出的耦合贝叶斯RPCA（BRPCA）的检测性能。我们发现，与传统的PCA和非耦合BRPCA相比，该方法显着提高了检测精度。

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