Graph Neural Networks Empowered Origin-Destination Learning for Urban Traffic Prediction

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chuanting Zhang, Guoqing Ma, Liang Zhang, Basem Shihada
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引用次数: 0

Abstract

Urban traffic prediction with high precision is always the unremitting pursuit of intelligent transportation systems and is instrumental in bringing smart cities into reality. The fundamental challenges for traffic prediction lie in the accurate modelling of spatial and temporal traffic dynamics. Existing approaches mainly focus on modelling the traffic data itself, but do not explore the traffic correlations implicit in origin-destination (OD) data. In this paper, we propose STOD-Net, a dynamic spatial-temporal OD feature-enhanced deep network, to simultaneously predict the in-traffic and out-traffic for each and every region of a city. We model the OD data as dynamic graphs and adopt graph neural networks in STOD-Net to learn a low-dimensional representation for each region. As per the region feature, we design a gating mechanism and operate it on the traffic feature learning to explicitly capture spatial correlations. To further capture the complicated spatial and temporal dependencies among different regions, we propose a novel joint feature, learning block in STOD-Net and transfer the hybrid OD features to each block to make the learning process spatiotemporal-aware. We evaluate the effectiveness of STOD-Net on two benchmark datasets, and experimental results demonstrate that it outperforms the state-of-the-art by approximately 5% in terms of prediction accuracy and considerably improves prediction stability up to 80% in terms of standard deviation.

Abstract Image

Abstract Image

Abstract Image

基于图神经网络的城市交通始发-目的地学习
高精度的城市交通预测一直是智能交通系统的不懈追求,是实现智慧城市的重要手段。交通预测的根本挑战在于对交通时空动态的准确建模。现有的方法主要关注交通数据本身的建模,而没有探索始发目的地(OD)数据隐含的交通相关性。本文提出了一种基于OD特征增强的动态时空深度网络——std - net,用于同时预测城市各区域的进出交通。我们将OD数据建模为动态图,并采用STOD-Net中的图神经网络来学习每个区域的低维表示。根据区域特征,我们设计了一种门控机制,并在交通特征学习上进行操作,以显式捕获空间相关性。为了进一步捕捉不同区域之间复杂的时空依赖关系,我们提出了一种新的联合特征——学习块,并将混合OD特征转移到每个块上,使学习过程具有时空感知。我们在两个基准数据集上评估了STOD-Net的有效性,实验结果表明,就预测精度而言,它比最先进的预测精度高出约5%,并且就标准偏差而言,它显着提高了预测稳定性,最高可达80%。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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