  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
