Complementary Fusion of Deep Network and Tree Model for ETA Prediction
<|reference_start|>Complementary Fusion of Deep Network and Tree Model for ETA Prediction: 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.<|reference_end|>
@article{huang2024complementary,
    title={Complementary Fusion of Deep Network and Tree Model for ETA Prediction},
    author={YuRui Huang, Jie Zhang, HengDa Bao, Yang Yang, Jian Yang},
    journal={arXiv preprint arXiv:2407.01262},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01262},
    primaryClass={cs.LG}
}