  Congestion prediction represents a major priority for traffic management
centres around the world to ensure timely incident response handling. The
increasing amounts of generated traffic data have been used to train machine
learning predictors for traffic, however this is a challenging task due to
inter-dependencies of traffic flow both in time and space. Recently, deep
learning techniques have shown significant prediction improvements over
traditional models, however open questions remain around their applicability,
accuracy and parameter tuning. This paper proposes an advanced deep learning
framework for simultaneously predicting the traffic flow on a large number of
monitoring stations along a highly circulated motorway in Sydney, Australia,
including exit and entry loop count stations, and over varying training and
prediction time horizons. The spatial and temporal features extracted from the
36.34 million data points are used in various deep learning architectures that
exploit their spatial structure (convolutional neuronal networks), their
temporal dynamics (recurrent neuronal networks), or both through a hybrid
spatio-temporal modelling (CNN-LSTM). We show that our deep learning models
consistently outperform traditional methods, and we conduct a comparative
analysis of the optimal time horizon of historical data required to predict
traffic flow at different time points in the future.
