@article{JIN2008, 
author = {Xuexiang JIN and Yi ZHANG and Li LI and Jianming HU},
title = {Robust PCA-Based Abnormal Traffic Flow Pattern Isolation and Loop Detector Fault Detection},
year = {2008},
journal = {Tsinghua Science and Technology},
volume = {13},
number = {6},
pages = {829-835},
keywords = {traffic flow pattern, robust principal components analysis (RPCA), loop detector faults},
url = {https://www.sciopen.com/article/10.1016/S1007-0214(08)72208-9},
doi = {10.1016/S1007-0214(08)72208-9},
abstract = {One key function of intelligent transportation systems is to automatically detect abnormal traffic phenomena and to help further investigations of the cause of the abnormality. This paper describes a robust principal components analysis (RPCA)-based abnormal traffic flow pattern isolation and loop detector fault detection method. The results show that RPCA is a useful tool to distinguish regular traffic flow from abnormal traffic flow patterns caused by accidents and loop detector faults. This approach gives an effective traffic flow data pre-processing method to reduce the human effort in finding potential loop detector faults. The method can also be used to further investigate the causes of the abnormality.}
}