The advancement of cognitive computing for traffic status understanding, powered by machine learning and data analytics, enables prediction of traffic anomalies from continuously generated big GPS trajectory data. Existing methods generally use traffic indicators such as traffic flows and speeds to detect anomalies, but they may over-identify anomalies while missing the critical ones. For example, they use historical anomalies to train the prediction model, but past anomalies may not be a perfect indication of future anomalies since anomalies are often rare. In this paper, we propose a novel cognitive approach, a Visible Outlier Indexes and Meshed Spatiotemporal Neighborhoods (VOI-MSN) method, to predict traffic anomalies from GPS trajectories. In the VOI-MSN method, two cognitive techniques are provided. The first is VOI, which measures the abnormal scores using overall samples and can be intuitively understood by humans. The second is MSN, which learns the dynamic impact range (i.e., spatiotemporal neighborhood) from historical trajectory data and provides a complete and exact analysis of the local traffic situation. It emulates human cognitive processing to adaptively judge the impact range by experience. The effectiveness of the proposed method is demonstrated using a massive trajectory dataset with 2.5 billion location records for 27,266 taxis, and it achieves higher precision and recall in predicting traffic anomalies than the counterpart methods. The VOI-MSN method achieves high accuracy and recall for predicting traffic anomalies. It outperforms traffic indicator–based (speed and traffic flow) methods, the fixed-size spatial neighborhood method and the causal network method.

中文翻译：

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从GPS轨迹使用可见的离群值指数和网格的时空邻域的认知交通异常预测。

借助机器学习和数据分析技术，用于交通状态理解的认知计算技术的进步，使得能够根据连续生成的大GPS轨迹数据预测交通异常。现有方法通常使用诸如交通流量和速度之类的交通指标来检测异常，但是它们可能会过度识别异常，而错过了关键异常。例如，他们使用历史异常来训练预测模型，但是过去的异常可能并不是未来异常的完美指示，因为异常通常很少见。在本文中，我们提出了一种新颖的认知方法，即可见异常值索引和网格时空邻域（VOI-MSN）方法，以根据GPS轨迹预测交通异常。在VOI-MSN方法中，提供了两种认知技术。首先是VOI，使用整体样本来测量异常分数，并且人类可以直观地理解。第二个是MSN，它从历史轨迹数据中了解动态影响范围（即时空邻域），并提供对本地交通状况的完整而准确的分析。它模拟人类的认知过程，以根据经验自适应地判断影响范围。使用庞大的轨迹数据集显示了该方法的有效性，该数据集具有针对27,266辆出租车的25亿个位置记录，并且在预测交通异常方面比对等方法具有更高的精度和召回率。VOI-MSN方法可实现较高的准确性和召回率，以预测流量异常。它优于基于流量指标的方法（速度和流量），