Complementary Fusion of Deep Network and Tree Model for ETA Prediction
This work addresses ETA estimation for transportation systems, representing an incremental improvement through ensemble methods.
The authors tackled the ETA prediction problem by proposing an ensemble of tree models and neural networks, achieving first place in the SIGSPATIAL 2021 GISCUP competition with proven accuracy and robustness.
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.