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Complementary Fusion of Deep Network and Tree Model for ETA Prediction

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arxiv 2407.01262 v1 pith:XKBCTUSY submitted 2024-07-01 cs.LG

YuRui Huang , Jie Zhang , HengDa Bao , Yang Yang , Jian Yang This is my paper · ORCID
classification cs.LG
keywords solutionsystemstransportationtreeaccuracyarrivalattentionsattracted
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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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