  Traffic flow prediction, particularly in areas that experience highly dynamic
flows such as motorways, is a major issue faced in traffic management. Due to
increasingly large volumes of data sets being generated every minute, deep
learning methods have been used extensively in the latest years for both short
and long term prediction. However, such models, despite their efficiency, need
large amounts of historical information to be provided, and they take a
considerable amount of time and computing resources to train, validate and
test. This paper presents two new spatial-temporal approaches for building
accurate short-term prediction along a popular motorway in Sydney, by making
use of the graph structure of the motorway network (including exits and
entries). The methods are built on proximity-based approaches, denoted
backtracking and interpolation, which uses the most recent and closest traffic
flow information for each of the target counting stations along the motorway.
The results indicate that for short-term predictions (less than 10 minutes into
the future), the proposed graph-based approaches outperform state-of-the-art
deep learning models, such as long-term short memory, convolutional neuronal
networks or hybrid models.
