Name
Abstract | ||
|---|---|---|
Traffic speed is a key indicator for the efficiency of an urban transportation system. Accurate modeling of the spatiotemporally varying traffic speed thus plays a crucial role in urban planning and development. This paper addresses the problem of efficient fine-grained traffic speed prediction using big traffic data obtained from static sensors. Gaussian processes (GPs) have been previously used ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TBDATA.2016.2620488 | IEEE Transactions on Big Data |
Keywords | DocType | Volume |
Roads,Sensors,Big data,Spatiotemporal phenomena,Urban areas,Gaussian processes,Predictive models | Journal | 3 |
Issue | ISSN | Citations |
2 | IEEE Transactions on Big Data, vol. 3, no. 2, pp. 194-207, 2017 | 4 |
PageRank | References | Authors |
0.48 | 26 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Truc Viet Le | 1 | 10 | 4.08 |
| Richard Jayadi Oentaryo | 2 | 80 | 10.00 |
| Siyuan Liu | 3 | 544 | 37.89 |
| Hoong Chuin Lau | 4 | 739 | 91.69 |