Name
Abstract | ||
|---|---|---|
It is expensive to collect trajectory data on a mobile phone by continuously pinpointing its location, which limits the application of trajectory data mining (e.g., trajectory prediction). In this poster, we propose a method for trajectory prediction by collecting cell-id trajectory data without explicit locations. First, it exploits the spatial correlation between cell towers based on graph embedding technique. Second, it employs the sequence-to-sequence (seq2seq) framework to train the prediction model by designing a novel spatial loss function. Experiment results based on real datasets have demonstrated the effectiveness of the proposed method.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3343764 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
Seq2Seq model, cell-id trajectory, graph embedding, spatial loss function | Computer vision,Cell ID,Computer science,Artificial intelligence,Trajectory | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingqi Lv | 1 | 18 | 3.81 |
| Dajian Zeng | 2 | 0 | 0.34 |
| Tieming Chen | 3 | 29 | 5.11 |
| Ling Chen | 4 | 1138 | 114.76 |