Name
Title | ||
|---|---|---|
Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting |
Abstract | ||
|---|---|---|
Intelligent transportation systems are a key component in smart cities, and the estimation and prediction of the spatiotemporal traffic state is critical to capture the dynamics of traffic congestion, i.e., its generation, propagation and mitigation, in order to increase operational efficiency and improve livability within smart cities. And while spatiotemporal data related to traffic is becoming ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TKDE.2019.2954868 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Roads,Tensile stress,Forecasting,Sensors,Urban areas,Junctions,Real-time systems | Journal | 33 |
Issue | ISSN | Citations |
6 | 1041-4347 | 3 |
PageRank | References | Authors |
0.38 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdelkader Baggag | 1 | 8 | 3.82 |
| Sofiane Abbar | 2 | 141 | 17.23 |
| Ankit Sharma | 3 | 22 | 6.81 |
| Tahar Zanouda | 4 | 3 | 0.38 |
| Abdulaziz Al-Homaid | 5 | 3 | 0.38 |
| Abhiraj Mohan | 6 | 3 | 0.38 |
| Jaideep Srivastava | 7 | 5845 | 871.63 |