1 research outputs found
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Deep Learning methods have been proven to be flexible to model complex
phenomena. This has also been the case of Intelligent Transportation Systems
(ITS), in which several areas such as vehicular perception and traffic analysis
have widely embraced Deep Learning as a core modeling technology. Particularly
in short-term traffic forecasting, the capability of Deep Learning to deliver
good results has generated a prevalent inertia towards using Deep Learning
models, without examining in depth their benefits and downsides. This paper
focuses on critically analyzing the state of the art in what refers to the use
of Deep Learning for this particular ITS research area. To this end, we
elaborate on the findings distilled from a review of publications from recent
years, based on two taxonomic criteria. A posterior critical analysis is held
to formulate questions and trigger a necessary debate about the issues of Deep
Learning for traffic forecasting. The study is completed with a benchmark of
diverse short-term traffic forecasting methods over traffic datasets of
different nature, aimed to cover a wide spectrum of possible scenarios. Our
experimentation reveals that Deep Learning could not be the best modeling
technique for every case, which unveils some caveats unconsidered to date that
should be addressed by the community in prospective studies. These insights
reveal new challenges and research opportunities in road traffic forecasting,
which are enumerated and discussed thoroughly, with the intention of inspiring
and guiding future research efforts in this field.Comment: 25 pages, 7 figures, 2 table