TY - JOUR
T1 - STLP-OD: Spatial and Temporal Label Propagation for Traffic Outlier Detection
AU - Pu, Juhua
AU - Wang, Yue
AU - Liu, Xinran
AU - Zhang, Xiangliang
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1002000,
Science Technology and Innovation Commission of Shenzhen Municipality JCYJ20180307123659504, and the State Key Laboratory of
Software Development Environment.
PY - 2019
Y1 - 2019
N2 - This paper focuses on the detection of non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations, and disasters. Comparing to existing approaches, it considers the spatial and temporal propagation of traffic anomalies from one road to other neighbor roads by proposing an STLP-OD framework. The experimental results on a real data set show that the proposed approach can improve the accuracy of traffic outlier detection baselines significantly.
AB - This paper focuses on the detection of non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations, and disasters. Comparing to existing approaches, it considers the spatial and temporal propagation of traffic anomalies from one road to other neighbor roads by proposing an STLP-OD framework. The experimental results on a real data set show that the proposed approach can improve the accuracy of traffic outlier detection baselines significantly.
UR - http://hdl.handle.net/10754/655896
UR - https://ieeexplore.ieee.org/document/8715411/
UR - http://www.scopus.com/inward/record.url?scp=85066454040&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2916853
DO - 10.1109/ACCESS.2019.2916853
M3 - Article
SN - 2169-3536
VL - 7
SP - 63036
EP - 63044
JO - IEEE Access
JF - IEEE Access
ER -
