WEST GCN-LSTM：用于区域交通预测的加权堆叠时空图神经网络
WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting
AI总结：
本文提出WEST GCN-LSTM加权堆叠时空图神经网络，通过共享边界策略和可调跳数策略融入区域与人群信息，在19个模型、多数据集评估中显著优于现有方法，并通过消融实验验证了各组件的有效性。
摘要：
区域交通预测是城市出行中的一项关键挑战，在万物互联等多个领域均有应用。近年来，时空图神经网络在众多交通预测挑战中取得了最先进的结果。本研究旨在扩展传统的时空图神经网络架构，以便纳入所研究区域以及穿行其中的人群信息，从而建立更高效的预测模型。这项科学工作的最终成果是一种新颖的时空图神经网络架构，称为WEST（WEighted STacked，加权堆叠）GCN-LSTM。此外，上述信息的纳入通过两种新颖的专用算法实现，分别称为Shared Borders Policy（共享边界策略）和Adjustable Hops Policy（可调跳数策略）。通过信息融合与蒸馏，所提出的解决方案在包含19个预测模型、跨多个数据集的实验评估中显著优于竞争对手。最后，额外的消融研究确定，所提出解决方案的每个组成部分都有助于提升其整体性能。
英文摘要：
Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims at expanding upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions, as well as the populations that traverse them, in order to establish a more efficient prediction model. The end-product of this scientific endeavour is a novel spatio-temporal graph neural network architecture that is referred to as WEST (WEighted STacked) GCN-LSTM. Furthermore, the inclusion of the aforementioned information is conducted via the use of two novel dedicated algorithms that are referred to as the Shared Borders Policy and the Adjustable Hops Policy. Through information fusion and distillation, the proposed solution manages to significantly outperform its competitors in the frame of an experimental evaluation that consists of 19 forecasting models, across several datasets. Finally, an additional ablation study determined that each of the components of the proposed solution contributes towards enhancing its overall performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
