Summary of Complementary Fusion Of Deep Network and Tree Model For Eta Prediction, by Yurui Huang et al.
Complementary Fusion of Deep Network and Tree Model for ETA Prediction
by YuRui Huang, Jie Zhang, HengDa Bao, Yang Yang, Jian Yang
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution for estimated time of arrival (ETA) estimation combines the strengths of tree models and neural networks within an ensemble framework. This novel approach is shown to be highly accurate and robust, as demonstrated through experiments on the A/B list dataset, which ultimately led to a first-place win in the SIGSPATIAL 2021 GISCUP competition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to guess how long it will take to get somewhere. That’s what estimated time of arrival (ETA) is all about. It’s super important for transportation systems and has become a key feature in navigation apps. The problem is, getting ETA right can be tricky! In this paper, scientists came up with a new way to do it by combining two different approaches: tree models and neural networks. They tested their idea on some data and proved that it works really well. |
