Name
Abstract | ||
|---|---|---|
Over 10 billion packages are picked up every day in China. Accurate prediction of couriers' pick-up routes can help the dispatch system to assign packages to couriers more intelligently, which is able to further increase the pick-up efficiency and reduce the overdue rate. In the package pick-up scene, the decision-making of a courier is quite complex since it's affected by strict spatial-temporal constraints (e.g., package location, promised pick-up time, current time and courier's current location). In this paper, we propose a novel model, named DeepRoute, to predict couriers' future package pick-up routes according to the couriers' decision experience learnt from their historical spatial-temporal behaviors. Specifically, DeepRoute consists of three layers: 1) The representation layer produces experience-aware representations for unpicked-up packages. 2) The transformer encoder layer encodes the representations of packages while considering the spatial-temporal correlations among them. 3) The attention-based decoder layer uses the attention mechanism to generate the whole pick-up route recurrently. Experiments on a real-world logistics dataset demonstrate the state-of-the-art performance of our DeepRoute model. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICDE51399.2021.00214 | 2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021) |
DocType | ISSN | Citations |
Conference | 1084-4627 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haomin Wen | 1 | 0 | 1.01 |
| Youfang Lin | 2 | 98 | 21.50 |
| fan wu | 3 | 7 | 7.88 |
| wan huaiyu | 4 | 72 | 11.46 |
| Shengnan Guo | 5 | 30 | 3.34 |
| LiXia Wu | 6 | 6 | 2.47 |
| Chao Song | 7 | 100 | 15.52 |
| Yinghui Xu | 8 | 172 | 20.23 |