  This work aims at unveiling the potential of Transfer Learning (TL) for
developing a traffic flow forecasting model in scenarios of absent data.
Knowledge transfer from high-quality predictive models becomes feasible under
the TL paradigm, enabling the generation of new proper models with few data. In
order to explore this capability, we identify three different levels of data
absent scenarios, where TL techniques are applied among Deep Learning (DL)
methods for traffic forecasting. Then, traditional batch learning is compared
against TL based models using real traffic flow data, collected by deployed
loops managed by the City Council of Madrid (Spain). In addition, we apply
Online Learning (OL) techniques, where model receives an update after each
prediction, in order to adapt to traffic flow trend changes and incrementally
learn from new incoming traffic data. The obtained experimental results shed
light on the advantages of transfer and online learning for traffic flow
forecasting, and draw practical insights on their interplay with the amount of
available training data at the location of interest.
