Recent presentation delivered for the IEEE ITSC 2020 online conference for joint collaboration with Dr. Marian-Andrei RIZOIU (http://rizoiu.eu/) and Zac Papachatgis from UTS.
PREPRINT available on my webpage:
http://www.simonamihaita.com/papers/IEEITSC2020_FINAL_version_Network_Traffic_Flow_prediction_PREPRINT.pdf
Abstract:
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 being generated every minute, deep learning
methods have been used extensively in the latest years for
both short and long term traffic flow 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 predictions along a popular
motorway in Sydney Australia, by making use of the graph
structure of the motorway network (including exits and entries).
Our proposed methods are proximity-based, and they use the
most recent available traffic flow information of the upstream
counting stations closest to a given target station. Where such
information is not available they employ daily historical means
instead. We show that for short-term predictions (less than 10
minutes into the future), our proposed graph-based approaches
outperform state-of-the-art deep learning models, such as longterm
short memory, convolutional neuronal networks or hybrid
models.
Add comment