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arXiv · 2407.01262

Complementary Fusion of Deep Network and Tree Model for ETA Prediction

YuRui Huang·Jie Zhang·HengDa Bao·Yang Yang·Jian Yang
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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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YuRui Huang, Jie Zhang, HengDa Bao, Yang Yang, Jian Yang. 2024-07-01. Complementary Fusion of Deep Network and Tree Model for ETA Prediction. https://arxiv.org/abs/2407.01262

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