Paper ID: 2407.01262

Complementary Fusion of Deep Network and Tree Model for ETA Prediction

YuRui Huang, Jie Zhang, HengDa Bao, Yang Yang, Jian Yang

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.

Submitted: Jul 1, 2024