A New Clustering Algorithm for Processing GPS-Based Road Anomaly Reports With a Mahalanobis Distance

作者：Li Zhaojian^*; Filev Dimitar P; Kolmanovsky Ilya; Atkins Ella; Lu Jianbo

来源：IEEE Transactions on Intelligent Transportation Systems, 2017, 18(7): 1980-1988.

DOI：10.1109/TITS.2016.2614350

• Evolving clustering algorithm
• Mahalanobis distance
• road anomaly report
• Woodbury matrix inversion lemma

This paper considers a new clustering algorithm for processing time-evolving road anomaly reports. Two cluster categories, main and outlier, are defined to deal with outliers as well as to capture the evolving nature of road anomalies. The Mahalanobis distance is exploited to quantify the similarity between a new report and the existing clusters. The clusters are maintained online and the Woodbury matrix inverse lemma is used for their recursive updates. The proposed clustering algorithm can localize isolated anomalies and compress information for densely distributed anomalies. A simulation is presented to demonstrate the efficacy of the proposed algorithm.

• 出版日期2017-7