Name
Abstract | ||
|---|---|---|
Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, we propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering information from the past is critical here, since taxi requests in the future are co... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TITS.2017.2755684 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Public transportation,Urban areas,Recurrent neural networks,Predictive models,Global Positioning System,Hidden Markov models,Real-time systems | Simulation,Recurrent neural network,Real time prediction,Long short term memory,Public transport,Artificial intelligence,Global Positioning System,Engineering,Artificial neural network,Hidden Markov model,Sequence learning,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 8 | 1524-9050 |
Citations | PageRank | References |
19 | 0.70 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Xu | 1 | 20 | 2.07 |
| Rouhollah Rahmatizadeh | 2 | 47 | 6.03 |
| Ladislau Boloni | 3 | 98 | 15.21 |
| Damla Turgut | 4 | 1127 | 87.39 |