时空图神经网络在交通流预测研究中的构建与应用综述

Review of Construction and Applications of Spatio-Temporal Graph Neural Network in Traffic Flow Prediction

交通流量预测是城市交通管理和规划中的关键问题,而传统预测方法在面对数据稀疏性、非线性关系和复杂动态性等挑战时表现不佳。图神经网络是一种基于非欧结构数据的深度学习方法,近年来在各种复杂网络建模和预测任务中得到广泛应用。为了应用于交通流量预测领域,提出了时空图神经网络,其能够捕捉空间和时间相关性,相较之前的预测模型有显著进步。对近年来使用时空图神经网络进行交通流量预测的模型进行分析,概述和比较了多种邻接阵的构造方式,然后从空间相关性和时间相关性的角度列举了构建交通流预测模型的常用组件,并对不同的时空融合方式进行了分类和对比;在应用方面,根据时间尺度的不同将时空图神经网络模型分为长期预测、短期预测与兼顾长短期的预测三类,分析了各自的目标与要求,并列举比较了近年来较为突出的新模型。最后,讨论了现有研究的局限性,对相关模型的未来研究做出展望。

The prediction of traffic flow is a pivotal concern within urban traffic management and planning,yet conven-tional forecasting techniques prove inadequate in addressing challenges like data sparsity,nonlinear associations,and intri-cate dynamics.Graph neural network is a deep learning approach based on non-Euclidean structural data,which has been widely used in various complex network modeling and predictive tasks in recent times.To address traffic flow prediction,a spatiotemporal graph neural network is proposed,which can capture spatial and temporal correlations,making signifi-cant progress compared to earlier predictive models.An analysis is conducted on models utilizing spatiotemporal graph neural network for the prediction of traffic flow in recent times Firstly,various construction methods of adjacency matri-ces are summarized and compared.Then,the common components of traffic flow prediction models are listed from the perspective of spatial correlation and temporal correlation,and different spatio-temporal fusion modes are classified and compared.On the application front,spatiotemporal graph neural network models are categorized into three classes based on temporal scales:long-term prediction,short-term prediction,and combined long-short-term prediction.Analysis of respective objectives and requisites is conducted,accompanied by enumeration and comparison of prominent recent models.Finally,limitations of existing research are deliberated upon,and prospects for future studies pertaining to rele-vant models are outlined.