深度网络与树模型互补融合的ETA预测

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI:10.1145/3474717.3488237

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

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引用次数: 1

摘要

预计到达时间(ETA)是运输系统中一个非常重要的因素。它作为导航系统和智能交通系统的一项基础服务，越来越受到人们的关注和广泛应用。在本文中，我们提出了一种新的解决ETA估计问题的方法，即树模型和神经网络的集成。我们在A/B列表上证明了解决方案的准确性和鲁棒性，并最终在SIGSPATIAL 2021 GISCUP竞赛中获得第一名。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Complementary Fusion of Deep Network and Tree Model for ETA Prediction

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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Proceedings of the 29th International Conference on Advances in Geographic Information Systems

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