Complementary Fusion of Deep Network and Tree Model for ETA Prediction

YuRui Huang, Jie Zhang, HengDa Bao, Yang Yang, Jian Yang
7/1/2024
cs.LG

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Abstract

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.

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Cite this paper

@article{huang2024complementary,
  title  = {Complementary Fusion of Deep Network and Tree Model for ETA Prediction},
  author = {YuRui Huang and Jie Zhang and HengDa Bao and Yang Yang and Jian Yang},
  year   = {2024},
  eprint = {2407.01262},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.01262v1}
}

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