基于流量-密度关系波动的交通流数据异常检测与分类
Anomaly detection and classification in traffic flow data from fluctuations in the flow-density relationship
摘要：
我们描述并验证了一种新的数据驱动的方法，实时检测和分类的交通异常的基础上识别的非典型波动的密度和流量之间的关系。对于静态条件下的聚合数据，流量和密度通过基本图相关联。然而，从现代传感器网络获得的高分辨率数据通常是非平稳且分散的。因此，这些数据显示出重大的统计波动。在密度流平面中，用二元概率分布来描述这些波动是最好的。通过应用核密度估计，从英国国家交通信息服务（NTIS）的大容量数据，我们凭经验构建这些分布伦敦的M25高速公路。然后构造密度流平面中的曲线，类似于单变量分布的分位数。这些曲线定量地将非典型波动与典型交通状态分开。虽然该算法识别异常的一般，而不是特定的事件，我们发现，95%的概率曲线以外的波动强烈相关的尖峰行程时间与重大拥堵事件。此外，偏离典型区域的偏移的大小提供了对检测到的异常的严重性的简单、实时的测量。我们通过基准测试其识别历史NTIS数据中标记事件的能力来验证该算法，并与文献中的一些常用方法进行比较。检测率，检测时间和虚警率被用作度量，并发现一般是可比的，除了在速度分布是双峰的情况下。在这种情况下，新算法实现了更低的误报率，而不会对其他指标造成显着退化。该方法具有自校准的额外优点。
英文摘要：
We describe and validate a novel data-driven approach to the real time detection and classification of traffic anomalies based on the identification of atypical fluctuations in the relationship between density and flow. For aggregated data under stationary conditions, flow and density are related by the fundamental diagram. However, high resolution data obtained from modern sensor networks is generally non-stationary and disaggregated. Such data consequently show significant statistical fluctuations. These fluctuations are best described using a bivariate probability distribution in the density-flow plane. By applying kernel density estimation to high-volume data from the UK National Traffic Information Service (NTIS), we empirically construct these distributions for London's M25 motorway. Curves in the density-flow plane are then constructed, analogous to quantiles of univariate distributions. These curves quantitatively separate atypical fluctuations from typical traffic states. Although the algorithm identifies anomalies in general rather than specific events, we find that fluctuations outside the 95\% probability curve correlate strongly with the spikes in travel time associated with significant congestion events. Moreover, the size of an excursion from the typical region provides a simple, real-time measure of the severity of detected anomalies. We validate the algorithm by benchmarking its ability to identify labelled events in historical NTIS data against some commonly used methods from the literature. Detection rate, time-to-detect and false alarm rate are used as metrics and found to be generally comparable except in situations when the speed distribution is bi-modal. In such situations, the new algorithm achieves a much lower false alarm rate without suffering significant degradation on the other metrics. This method has the additional advantage of being self-calibrating.
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