  Advanced travel information and warning, if provided accurately, can help
road users avoid traffic congestion through dynamic route planning and behavior
change. It also enables traffic control centres mitigate the impact of
congestion by activating Intelligent Transport System (ITS) proactively. Deep
learning has become increasingly popular in recent years, following a surge of
innovative GPU technology, high-resolution, big datasets and thriving machine
learning algorithms. However, there are few examples exploiting this emerging
technology to develop applications for traffic prediction. This is largely due
to the difficulty in capturing random, seasonal, non-linear, and
spatio-temporal correlated nature of traffic data. In this paper, we propose a
data-driven modelling approach with a novel hierarchical D-CLSTM-t deep
learning model for short-term traffic speed prediction, a framework combined
with convolutional neural network (CNN) and long short-term memory (LSTM)
models. A deep CNN model is employed to learn the spatio-temporal traffic
patterns of the input graphs, which are then fed into a deep LSTM model for
sequence learning. To capture traffic seasonal variations, time of the day and
day of the week indicators are fused with trained features. The model is
trained end-to-end to predict travel speed in 15 to 90 minutes in the future.
We compare the model performance against other baseline models including CNN,
LGBM, LSTM, and traditional speed-flow curves. Experiment results show that the
D-CLSTM-t outperforms other models considerably. Model tests show that speed
upstream also responds sensibly to a sudden accident occurring downstream. Our
D-CLSTM-t model framework is also highly scalable for future extension such as
for network-wide traffic prediction, which can also be improved by including
additional features such as weather, long term seasonality and accident
information.
