Robust PCA-Based Abnormal Traffic Flow Pattern Isolation and Loop Detector Fault Detection |
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Authors: | Xuexiang Jin êý Yi Zhang ¼ Li Li Jianming Hu
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Institution: | aDepartment of Automation, Tsinghua University, Beijing 100084, China |
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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. |
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Keywords: | traffic flow pattern robust principal components analysis (RPCA) loop detector faults |
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