Complementary Fusion of Deep Network and Tree Model for ETA Prediction
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911939188228096 |
|---|---|
| author | Huang, YuRui Zhang, Jie Bao, HengDa Yang, Yang Yang, Jian |
| author_facet | Huang, YuRui Zhang, Jie Bao, HengDa Yang, Yang Yang, Jian |
| contents | Estimated time of arrival (ETA) is a very important factor in the transportation system. It has attracted increasing attentions and has been widely used as a basic service in navigation systems and intelligent transportation systems. In this paper, we propose a novel solution to the ETA estimation problem, which is an ensemble on tree models and neural networks. We proved the accuracy and robustness of the solution on the A/B list and finally won first place in the SIGSPATIAL 2021 GISCUP competition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01262 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Complementary Fusion of Deep Network and Tree Model for ETA Prediction Huang, YuRui Zhang, Jie Bao, HengDa Yang, Yang Yang, Jian Machine Learning Estimated time of arrival (ETA) is a very important factor in the transportation system. It has attracted increasing attentions and has been widely used as a basic service in navigation systems and intelligent transportation systems. In this paper, we propose a novel solution to the ETA estimation problem, which is an ensemble on tree models and neural networks. We proved the accuracy and robustness of the solution on the A/B list and finally won first place in the SIGSPATIAL 2021 GISCUP competition. |
| title | Complementary Fusion of Deep Network and Tree Model for ETA Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.01262 |