Name
Abstract | ||
|---|---|---|
Traffic congestion has been an important problem all over the world. It is necessary to discover meaningful traffic patterns such as congestion propagation patterns from the massive historical dataset. Existed methods focusing on discovering congestion propagation patterns can’t mine transitivity of time and space very well. The spatio-temporal co-location pattern mining discovers the subsets of features which are located together in adjacent time periods frequently. So we propose using the spatio-temporal co-location pattern mining to discover congestion propagation patterns. Firstly, we propose the concepts of Spatio-Temporal Congestion Co-location Pattern (STCCP). Secondly, we give a framework and an algorithm for mining STCCPs. Finally, we validate our algorithm on real data sets. The results show that our method can effectively discover congestion propagation patterns. |
Year | Venue | Field |
|---|---|---|
2018 | APWeb/WAIM Workshops | Data mining,Data set,Computer science,Artificial intelligence,Machine learning,Traffic congestion,Transitive relation |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| He Ying | 1 | 20 | 5.70 |
| Lizhen Wang | 2 | 153 | 26.16 |
| Yuan Fang | 3 | 16 | 7.74 |
| Yurui Li | 4 | 2 | 0.70 |