TY - GEN
T1 - Temporal outlier detection in vehicle traffic data
AU - Li, Xiaolei
AU - Li, Zhenhui
AU - Han, Jiawei
AU - Lee, Jae Gil
PY - 2009
Y1 - 2009
N2 - Outlier detection in vehicle traffic data is a practical problem that has gained traction lately due to an increasing capability to track moving vehicles in city roads. In contrast to other applications, this particular domain includes a very dynamic dimension: time. Many existing algorithms have studied the problem of outlier detection at a single instant in time. This study proposes a method for detecting temporal outliers with an emphasis on historical similarity trends between data points. Outliers are calculated from drastic changes in the trends. Experiments with real world traffic data show that this approach is effective and efficient.
AB - Outlier detection in vehicle traffic data is a practical problem that has gained traction lately due to an increasing capability to track moving vehicles in city roads. In contrast to other applications, this particular domain includes a very dynamic dimension: time. Many existing algorithms have studied the problem of outlier detection at a single instant in time. This study proposes a method for detecting temporal outliers with an emphasis on historical similarity trends between data points. Outliers are calculated from drastic changes in the trends. Experiments with real world traffic data show that this approach is effective and efficient.
UR - http://www.scopus.com/inward/record.url?scp=67649653756&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67649653756&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2009.230
DO - 10.1109/ICDE.2009.230
M3 - Conference contribution
AN - SCOPUS:67649653756
SN - 9780769535456
T3 - Proceedings - International Conference on Data Engineering
SP - 1319
EP - 1322
BT - Proceedings - 25th IEEE International Conference on Data Engineering, ICDE 2009
T2 - 25th IEEE International Conference on Data Engineering, ICDE 2009
Y2 - 29 March 2009 through 2 April 2009
ER -