Traffic flow forecasting is a crucial first step in intelligent and proactive
traffic management. Traffic flow parameters are volatile and uncertain, making
traffic flow forecasting a difficult task if the appropriate forecasting model
is not used. Additionally, the non-Euclidean data structure of traffic flow
parameters is challenging to analyze from both spatial and temporal
perspectives. State-of-the-art deep learning approaches use pure convolution,
recurrent neural networks, and hybrid methods to achieve this objective
efficiently. However, many of the approaches in the literature rely on complex
architectures that can be difficult to train. This complexity also adds to the
black-box nature of deep learning. This study introduces a novel deep learning
architecture, referred to as the multigraph convolution neural network (MGCNN),
for turning movement prediction at intersections. The proposed architecture
combines a multigraph structure, built to model temporal variations in traffic
data, with a spectral convolution operation to support modeling the spatial
variations in traffic data over the graphs. The proposed model was tested using
twenty days of flow and traffic control data collected from an arterial in
downtown Chattanooga, TN, with ten signalized intersections. The model's
ability to perform short-term predictions over 1, 2, 3, 4, and 5 minutes into
the future was evaluated against four baseline state-of-the-art models. The
results showed that our proposed model is superior to the other baseline models
in predicting turning movements with a mean squared error (MSE) of 0.9

交通流量预测是智能主动交通管理的关键第一步。本研究引入了一种新型的深度学习架构，称为多图卷积神经网络（MGCNN），用于交叉口的转弯动作预测，并通过在时间和空间上建模交通数据的变化来改善预测性能。在实验中，该模型在对比其他四种基准模型的情况下表现出更好的预测结果，平均均方误差（MSE）为 0.9。