# Complementary Fusion of Deep Network and Tree Model for ETA Prediction

arXiv: https://arxiv.org/abs/2407.01262 · HTML: https://mlsanity.com/paper/2407.01262/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport](https://mlsanity.com/paper/2104.08489/) (2021)

## Built on by (most cited first, 2 of 2)

- [OBSR: Open Benchmark for Spatial Representations](https://mlsanity.com/paper/2510.05879/) (2025)
- [Verification of Adaptive Agentic Controllers through Finite Rule Revision](https://mlsanity.com/paper/2607.09770/) (2026)

## Abstract (arXiv)

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.