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首页> 美国政府科技报告 >Use of Mahalanobis Distance for Detecting Outliers and Outlier Clusters in Markedly Non-Normal Data: A Vehicular Traffic Example
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Use of Mahalanobis Distance for Detecting Outliers and Outlier Clusters in Markedly Non-Normal Data: A Vehicular Traffic Example

机译：使用马哈拉诺比斯距离检测显着非正态数据中的异常值和异常值群集：车载流量示例

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摘要

Modeling the behavior of interacting humans in routine but complex activities has many challenges, not the least of which is that humans can be both purposive and negligent, and further can encounter unexpected environmental hazards requiring fast action. The challenge is to characterize and model the humdrum routine while at the same time capturing the deviations and anomalies which arise from time to time. Because of the disruptive impact that anomalies (such as accidents) can have and the importance for incorporating them in our models, this report focuses on one technique for identifying anomalies in complex behavior patterns especially when there is no sharp demarcation between routine and unusual activity. The technique we evaluate is that of Mahalanobis distance which is known to be useful for identifying outliers when data is multivariate normal. But, the data we use for evaluation is deliberately markedly non-multivariate normal since that is what we confront in complex human systems. Specifically, we use one year's (2008) hourly traffic-volume data on a major multi-lane road (I-95) in one location in a major city (New York) with a dense population and several alternate routes. The traffic data is rich, large, incomplete, and reflects the effects of bad weather, accidents, routine fluctuations (rush hours versus dead of night), and onetime social events. The results show that Mahalanobis distance is a useful technique for identifying both single-hour outliers and contiguous-time clusters whose component members are not, in themselves, highly deviant.

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