Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2916853 | IEEE ACCESS |
Keywords | Field | DocType |
Traffic outlier detection,label propagation,coupled hidden Markov model | Anomaly detection,Pattern recognition,Computer science,Label propagation,Artificial intelligence,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juhua Pu | 1 | 50 | 11.90 |
| Yue Wang | 2 | 960 | 143.63 |
| Xinran Liu | 3 | 28 | 13.23 |
| Xiangliang Zhang | 4 | 728 | 87.74 |