Name
Title | ||
|---|---|---|
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach. |
Abstract | ||
|---|---|---|
•Two data fusion approaches for combining time-series with text data are proposed.•Text data is modeled using word embeddings, convolutional layers and attention.•Proposed models are applied to taxi demand prediction in event areas in New York.•Text information about events is shown to significantly reduce prediction error.•Proposed models are shown to substantially outperform traditional approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.inffus.2018.07.007 | Information Fusion |
Keywords | Field | DocType |
Deep learning,Data fusion,Cross modality learning,Time series forecasting,Textual data,Taxi demand,Special events,Urban mobility | Time series,Leverage (finance),Demand forecasting,Sensor fusion,Neglect,Temporal database,Artificial intelligence,Intelligent transportation system,Deep learning,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
49 | 1566-2535 | 10 |
PageRank | References | Authors |
0.50 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filipe Rodrigues | 1 | 97 | 8.80 |
| ioulia markou | 2 | 13 | 1.99 |
| Francisco C. Pereira | 3 | 331 | 33.07 |