Name
Abstract | ||
|---|---|---|
In this paper, we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behavior. Then, we explain how we model main traffic flow patterns by a mixture of 2-D Gaussian distributions. This yielded a density-based clustering of locations, which produces a data ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TITS.2017.2749413 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Trajectory,Public transportation,Predictive models,Roads,Data models,Markov processes,Vehicles | Cluster (physics),Data mining,Traffic flow,Data-driven,Gaussian,Artificial intelligence,Cluster analysis,Grid,Mathematics,Trajectory,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 8 | 1524-9050 |
Citations | PageRank | References |
9 | 0.54 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philippe Besse | 1 | 19 | 3.09 |
| Brendan Guillouet | 2 | 9 | 0.54 |
| Jean-Michel Loubes | 3 | 43 | 11.63 |
| Francois Royer | 4 | 9 | 0.54 |